Osteocytes regulate senescence of bone and bone marrow

  1. Peng Ding
  2. Chuan Gao
  3. Youshui Gao
  4. Delin Liu
  5. Hao Li
  6. Jun Xu
  7. Xiaoyi Chen
  8. Yigang Huang
  9. Changqing Zhang  Is a corresponding author
  10. Minghao Zheng  Is a corresponding author
  11. Junjie Gao  Is a corresponding author
  1. Department of Orthopaedics, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, China
  2. Centre for Orthopaedic Translational Research, Medical School, University of Western Australia, Australia
  3. Perron Institute for Neurological and Translational Science, Australia
  4. Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, China
  5. Institute of Microsurgery on Extremities, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, China

Abstract

The skeletal system contains a series of sophisticated cellular lineages arising from the mesenchymal stem cells (MSCs) and hematopoietic stem cells (HSCs) that determine the homeostasis of bone and bone marrow. Here, we reasoned that osteocyte may exert a function in regulation of these lineage cell specifications and tissue homeostasis. Using a mouse model of conditional deletion of osteocytes by the expression of diphtheria toxin subunit α in dentin matrix protein 1 (DMP1)-positive osteocytes, we demonstrated that partial ablation of DMP1-positive osteocytes caused severe sarcopenia, osteoporosis, and degenerative kyphosis, leading to shorter lifespan in these animals. Osteocytes reduction altered mesenchymal lineage commitment, resulting in impairment of osteogenesis and induction of osteoclastogensis. Single-cell RNA sequencing further revealed that hematopoietic lineage was mobilized toward myeloid lineage differentiation with expanded myeloid progenitors, neutrophils, and monocytes, while the lymphopoiesis was impaired with reduced B cells in the osteocyte ablation mice. The acquisition of a senescence-associated secretory phenotype (SASP) in both osteogenic and myeloid lineage cells was the underlying cause. Together, we showed that osteocytes play critical roles in regulation of lineage cell specifications in bone and bone marrow through mediation of senescence.

Editor's evaluation

The work provides a new understanding of the role of osteocytes in regulating other lineage cells in bone, bone marrow, and skeletal muscle. The set of data from the genetic mouse model, bone phenotypic analyses, and scRNA-seq analysis supports the conclusion. This is an important and logically presented study that offers new insight into the biology of osteocytes.

https://doi.org/10.7554/eLife.81480.sa0

eLife digest

A hallmark of aging is the weakening of our muscles and bones, which become more fragile as we get older. These gradual changes can result in a humpback and muscle shrinking among other conditions. At the same time little is known about what role osteocytes – the most abundant type of bone cell – play in the process of bone and muscle aging.

One way to investigate the role of osteocytes in aging is to remove them and observe what happens to nearby cells as they age. To achieve this Ding, Gao, Gao et al. genetically altered mice so that they would carry and activate a gene called DTA in their osteocytes. DTA is a gene derived from the bacterium that causes diphtheria, and when it is activated, it produces a toxin that accumulates in cells, eventually killing them. In the mice line developed by Ding, Gao, Gao et al. DTA slowly killed osteocytes, leading to adult mice lacking most of their osteocyte population that have a normal embryonic development. This is important because the fact that the mice develop normally before birth allowed the team to rule out embryonic defects when looking at their results.

Ding, Gao, Gao et al. found that, without enough osteocytes, the nearby bone and bone marrow cells aged faster than expected. Indeed, the skeleton and muscles of adult mice was severely affected by the loss of osteocytes, leading to fragile bones with lower mass and muscle shrinking. These mice looked old in their young age and died earlier.

At the cellular level, the removal of osteocytes impaired the formation of osteoblasts, the cells that are responsible for making bones. It also led to an increase in the numbers of osteoclasts – the cells that destroy bone tissue to repair it and maintain it – and fat tissue cells. Furthermore, cells in the bone marrow, which go on to make white blood cells, were also affected. The mechanisms through which osteocytes affect the growth of these other cells is yet to be fully understood. However, Ding, Gao, Gao et al. did observe that these cells acquired traits characteristic of aging cells, implying that osteocytes have a role in regulating cellular aging or senescence. Among these senescence traits is the increased production and secretion of molecules that interact with the immune system, a feature known as the ‘senescence-associated secretory phenotype’.

Overall, the results of Ding, Gao, Gao et al. suggest that reducing the number of osteocytes in mice leads to faster bone aging and affects the balance of the different cell types required for healthy bone and bone marrow growth. Future research could focus on finding drugs that allow osteocytes to keep performing their role during aging, and thus help maintain bone health. The findings of Ding, Gao, Gao et al. also suggest that osteocytes may be playing a previously underappreciated role in age-related diseases, which warrants further investigation.

Introduction

The skeletal system is an elaborate organ mainly containing bone, bone marrow, and other connective tissues, whose function includes movement, support, hematopoiesis, immune responses, and endocrine regulation (Karsenty and Ferron, 2012; Katsnelson, 2010; Quarles, 2011). The skeletal system hosts at least more than 12 types of cell lineages arising from hematopoietic stem cells (HSCs) and mesenchymal stem cells (MSCs) (Méndez-Ferrer et al., 2010). During hematopoiesis, HSCs give rise to lymphoid and myeloid lineage cells, including B cells, neutrophils, monocytes, as well as osteoclasts. Meanwhile, MSCs differentiate into osteoblastic lineage cells, bone marrow adipocytes, and form fibroconnective tissues. The sophisticated processes of differentiation and interaction of these cell lineages are critical not only to skeletal development, but also to the integrity of hematopoietic, immune, and endocrine systems (Méndez-Ferrer et al., 2010; Le et al., 2018; Yu and Scadden, 2016). During aging, these cell lineage commitments change rigorously and cause imbalance between myeloid–lymphoid hematopoiesis and adipo-osteogenic differentiation (Chen et al., 2016; Sinha et al., 2022), which lead to the increased myelopoiesis and adipogenesis as opposed to lymphopoiesis and osteogenesis. While the complex communications between these cell lineages have been documented, it is still unclear what determines these cell lineages to survive and how their cell fates are maintained during development and aging. It has been speculated that cellular senescence, characterized by cell proliferation arrest, altered metabolism, and apoptosis resistance (Gorgoulis et al., 2019; Tchkonia et al., 2013), may be responsible for the regulation of lineage cell fates. However, the precise role in aging and age-related diseases remains unclear.

Osteocytes, as the long-living terminally differentiated cells and the most abundant cells within the bone matrix (Tresguerres et al., 2020), play vital roles in maintaining the skeletal homeostasis. Apart from mechanical transduction (Long, 2011; Sato et al., 2020), osteocytes have been shown to regulate bone formation, bone resorption, bone marrow hematopoiesis (Asada et al., 2013; Azab et al., 2020; Fulzele et al., 2013; Xiao et al., 2021), and generate endocrine signals to mediate function of other organs (Razzaque, 2009; Fulzele et al., 2017; Cain et al., 2012). Osteocytes regulate both the osteoblast and osteoclast activities during bone remodeling (Delgado-Calle and Bellido, 2022; Tresguerres et al., 2020). Sclerostin, one of the key inhibitors of Wnt signaling pathway, is mainly produced by osteocytes (Tresguerres et al., 2020). NO and PGE2 secretion by osteocytes in response to mechanical stimulation have anabolic effects on osteoblasts (Rochefort et al., 2010). Receptor activation of nuclear factor-κ B ligand (RANKL), the osteoclast differentiation factor, is mainly produced by osteocytes (Nakashima et al., 2011). Osteocytes regulate neutrophil development through secretion of soluble factors like IL19 (Xiao et al., 2021) and can also regulate myelopoiesis through Gsα-dependent and -independent pathways (Fulzele et al., 2013; Azab et al., 2020). In addition, studies have shown that aging is associated with dysfunction of osteocytes. Degeneration of osteocytes lacuna-canalicular network had been observed in older adults (Busse et al., 2010) and the aging animal model (Tiede-Lewis et al., 2017). Senescent osteocytes and their senescence-associated secretory phenotype (SASP) have been shown to contribute to age-related bone loss (Farr et al., 2016; Kim et al., 2020). Together, current data suggest that osteocyte is a singling cell that coordinates activities of bone and bone marrow during skeletal aging (Sfeir et al., 2022).

Here, we hypothesize that coordination of bone and bone marrow homeostasis requires the presence of functional osteocytes. Reduction of osteocytes and their function may result in the detrimental impact in altering lineage cell fates and specifications in bone marrow. Using a mouse model of conditional deletion of osteocytes by the expression of diphtheria toxin subunit α (DTA) in dentin matrix protein 1 (DMP1)-positive osteocytes, we showed that osteocytes regulated bone and bone marrow lineage cell specification. Ablation of osteocytes in these mice caused impairment of osteogenesis and lymphopoiesis, and increased osteoclastogenesis and mobilization of myelopoiesis toward myeloid lineage differentiation with expanded myeloid progenitors, neutrophils, and monocytes. These have resulted in the induction of accelerated skeletal aging. Mice with osteocyte ablation have severe sarcopenia, osteoporosis, and kyphosis at the early stage of 13 weeks, resulting in shorter lifespan. Together, we demonstrated that osteocytes play a critical role in regulation of the HSC and MSC lineage cell differentiations by mediation of senescence.

Results

Mice with fewer osteocytes have severe osteoporosis, kyphosis, sarcopenia, and shorter lifespan

To delineate the role of osteocyte in skeletal tissue development and maturation, we established a mouse model based on DTA-mediated cell knockout using the promoter of DMP1 (Breitman et al., 1990). The latter is a protein highly expressed in late-stage osteocytes but has been shown not to be essential for early skeletal development (Feng et al., 2003). The results showed that complete ablation of DMP1-positive osteocytes (osteocyteDMP1) in Dmp1cre Rosa26em1Cin(SA-IRES-Loxp-ZsGreen-stop-Loxp-DTA) homozygotes (DTAho) caused lethality of mice before birth. This has led us to investigate the impact of partial ablation of osteocytes using Dmp1cre Rosa26em1Cin(SA-IRES-Loxp-ZsGreen-stop-Loxp-DTA) heterozygotes (DTAhet). Interestingly, Alizarin red/Alcian blue staining of whole-mount skeleton at E19.0 showed no apparent differences of craniofacial, long bones or spines between WT and DTAhet mice (Figure 1—figure supplement 1A). As shown in Figure 1A and B, DTAhet mice had more empty lacunae without the presence of osteocytes within cortical and trabecular bone matrix compared to WT mice. Further, reduced dendrites were also observed in residual osteocytes of DTAhet mice (Figure 1C and D), indicating the impairment of osteocyte network. To define how osteocyte partial ablation was achieved, we performed the quantification of empty lacunae ratio of DTAhet mice at 13 weeks. About 80% empty lacunae was observed in DTAhet mice at 13 weeks, which increased by about 20% compared to 4 weeks (Figure 1—figure supplement 1B and C), indicating that diphtheria toxin (DT) had an accumulative effect with age in DTAhet mice. Together, these results indicated that although there was partial ablation of osteocyteDMP1 in DTAhet mice, the embryonic development of skeletal tissue appeared to be normal.

Figure 1 with 1 supplement see all
DTAhet mice display partial osteocyte ablation.

(A, B) Hematoxylin–eosin staining of WT and DTAhet mice femur at 4 weeks (A) and quantification of the ratio of empty lacunae (arrows) (B) (n = 8–12 per group), indicating reduced osteocyte number in DTAhet mice. Scare bar, 20 µm. (C, D) Immunofluorescence staining of femoral cortical bone of 4-week-old WT and DTAhet mice (C) and quantification of dendrites per osteocyte based on the images (D) (n = 152 osteocytes in WT group and n = 64 osteocytes in DTAhet group). Scare bar, 20 µm. Error bar represents the standard deviation.

Figure 1—source data 1

DTAhet mice display partial osteocyte ablation.

https://cdn.elifesciences.org/articles/81480/elife-81480-fig1-data1-v2.xlsx

Next, we investigated whether reduction of osteocyteDMP1 in DTAhet mice had an impact on postnatal maturation of bone tissue. Micro-computed tomography (μCT) examination of the appendicular skeleton revealed a significant decrease in femur bone mineral density (BMD), bone volume fraction (BV/TV), trabecular number (Tb.N), trabecular thickness (Tb.Th), as well as greater trabecular separation (Tb.Sp) in DTAhet mice compared to those in WT mice at 4 weeks (Figure 2A and B). Moreover, ablation of osteocytes also led to cortical bone loss with decreased cortical thickness (Ct.Th) and increased cortical porosity (Ct.Po) (Figure 2A and C). At 13 weeks, DTAhet mice exhibited more bone loss in both trabecular and cortical bone compared to those in WT mice (Figure 2D–G). The progressive bone loss was observed through the life of DTAhet mice. The phenotype observed is unique and gender-insensitive (Figure 2—figure supplement 1A–C). Similarly, μCT observation of axial skeleton also revealed the significant bone loss in vertebral bodies (Figure 2H and I, Figure 2—figure supplement 1D and E). Furthermore, there was no increase of bone mass of vertebral bodies from 4 to 13 weeks in DTAhet mice (Figure 2J and K), suggesting the retardation of vertebral body maturation. At 13 weeks, obvious kyphosis occurred in DTAhet mice (Figure 2L) due to serve osteoporosis and vertebral body compression. Whole-body μCT scan revealed that there was a giant increase of thoracic and lumbar curvature of DTAhet mice (Figure 2M). At the age of 30 weeks, almost all of DTAhet mice developed severe kyphosis (Figure 2N). Consistent with the development of kyphosis, gait analysis revealed that DTAhet mice at 4 weeks had abnormal steps when running (Figure 2—figure supplement 2A and B). The front and hind stride length were much shorter in DTAhet mice (Figure 2—figure supplement 2C). Also, the swing speed of DTAhet mice was much slower than WT mice (Figure 2—figure supplement 2D, Videos 16).

Figure 2 with 2 supplements see all
Osteocyte ablation induces severe osteoporosis and kyphosis.

(A–C) Representative micro-computed tomography (µCT) reconstructive images of male WT and DTAhet mice femur at 4 weeks (A) and trabecular microstructural parameters (BMD, bone mineral density; BV/TV, bone volume fraction; Tb.N, trabecular number; Tb.Sp, trabecular separation; , Tb.Th, trabecular thickness) (B) and cortical microstructural parameters (Ct.Th, cortical thickness; Ct.Po, cortical porosity) (C) derived from µCT analysis (n = 4–7 per group). (D–G) Representative µCT reconstructive images of male WT and DTAhet mice femur at 13 weeks (D) and trabecular microstructural parameters (BMD, BV/TV, Tb.N, Tb.Sp, and Tb.Th) (E, F) and cortical microstructural parameters (Ct.Th and Ct.Po) (G) derived from µCT analysis (n = 3 per group), demonstrating severe bone loss in DTAhet mice. (H, I) Representative µCT reconstructive images of male WT and DTAhet mice third lumbar at 4 weeks (H) and trabecular microstructural parameters (BMD, BV/TV, Tb.N, Tb.Sp, and Tb.Th) (I) derived from µCT analysis (n = 4–7 per group). (J, K) Representative µCT reconstructive images of male WT and DTAhet mice third lumbar at 13 weeks (J) and trabecular microstructural parameters (BMD, BV/TV, Tb.N, Tb.Sp, and Tb.Th) (K) derived from µCT analysis (n = 3 per group), showing vertebral body bone loss in the spine of DTAhet mice. (L) Gross images of male WT and DTAhet mice at 13 weeks. (M) Representative whole-body µCT reconstructive and sagittal images of male WT and DTAhet mice at 13 weeks. (N) Representative whole-body µCT reconstructive and sagittal images of male DTAhet mice at 37 weeks, noting that severe kyphosis occurred in DTAhet mice. Error bar represents the standard deviation.

Figure 2—source data 1

Osteocyte ablation induces severe osteoporosis in male mice.

https://cdn.elifesciences.org/articles/81480/elife-81480-fig2-data1-v2.xlsx
Video 1
Representative movie showing movement in WT mice at 4 weeks.
Video 2
Representative movie showing movement defects in DTAhet mice at 4 weeks.
Video 3
Representative movie showing movement in WT mice at 13 weeks.
Video 4
Representative movie showing movement defects in DTAhet mice at 13 weeks.
Video 5
Representative movie showing movement in WT mice at 37 weeks.
Video 6
Representative movie showing movement defects in DTAhet mice at 37 weeks.

Whole-body examination of DTAhet mice revealed there was a continual body weight loss and muscle weight loss (Figure 3A–C) from 4 weeks. Histology examination of gastrocnemius muscles revealed focal muscle atrophy with mild inflammation at 4 weeks (Figure 3D and E). Many myonuclei were mispositioned and became centralized in contrast to those in WT mice. No muscle fibrosis was observed. At 13 weeks, there was continual muscle atrophy, rimmed vacuoles, and inclusion bodies seen within the muscle fibers (Figure 3F and G). To preclude the direct target of DMP1 on muscle, we quantified the number of muscle fibers and the results showed that there was no reduction of numbers of muscle fibers after osteocyte ablation at 4 weeks (Figure 3—figure supplement 1A) and 13 weeks in DTAhet mice compared to WT mice (Figure 3—figure supplement 1B). Measurement of Dmp1 expression in WT muscle showed that the level of Dmp1 expression in muscle was very weak and far less than bone (Figure 3—figure supplement 1C). Together, these results suggested that DTAhet mice had systemic muscle atrophy and sarcopenia. It is most likely that sarcopenia was caused by the impairment of osteocyte-muscle crosstalk. Analysis of lifespan in these mice further revealed the average lifespan of DTAhet mice was about 20–40 weeks, which was much shorter than WT mice (Figure 3H). Together, these data demonstrated that osteocytes ablation caused severe osteoporosis and kyphosis, as well as sarcopenia, which occurred at the very early stage compared to normal aging process. These age-related skeletal phenotypes combined with shortened lifespan demonstrated that osteocyte ablation led to the accelerated skeletal aging.

Figure 3 with 1 supplement see all
Osteocyte ablation leads to severe sarcopenia and shorter lifespan.

(A, B) Gross images (A) and weight (B) of male WT and DTAhet mice at 4 weeks (n = 5–8 per group). (C) The ratio of gastrocnemius muscle weight in WT and DTAhet mice at 4 weeks (n = 3 per group). (D, E) Hematoxylin–eosin staining of WT and DTAhet mice gastrocnemius muscle at 4 weeks (D) and quantification of myonuclear number per area fiber (n = 11 per group) and centralized nucleus per field (E) (n = 5 per group), showing focal muscle atrophy, increased centralized myonuclei, and mild inflammation in DTAhet mice. Scale bar, 20 µm. (F, G) Hematoxylin–eosin staining of WT and DTAhet mice gastrocnemius muscle at 13 weeks (F) and quantification of myonuclear number per area fiber (n = 11 per group) and centralized nucleus per field (G) (n = 6 per group), noting muscle atrophy, rimmed vacuoles, and inclusion bodies within the muscle fibers in DTAhet mice. Scale bar, 20 µm. (H) Kaplan–Meier survival curve of WT and DTAhet mice (n = 4–5 per group), showing that DTAhet mice had shorter lifespan than that of WT mice. Error bar represents the standard deviation.

Figure 3—source data 1

Osteocyte ablation leads to severe sarcopenia and shorter lifespan.

https://cdn.elifesciences.org/articles/81480/elife-81480-fig3-data1-v2.xlsx

Ablation of osteocytes alters mesenchymal lineage commitment and promotes osteoclastogensis

To explore the potential mechanism of why reduction of osteocytes has caused severe osteoporosis and kyphosis, RNA sequencing was performed on whole bone with bone marrow flushed out from DTAhet and WT mice at 4 weeks. Selected skeleton-related Gene Ontology (GO) analysis revealed that downregulated genes by osteocyte ablation were enriched in ossification, osteoblast differentiation, positive regulation of osteoblast differentiation, endochondral ossification, and bone morphogenesis (Figure 4—figure supplement 1A and Supplementary file 1). Heatmap of significantly differentiated genes (fold change >2.0-fold, WT average FPKM > 10, false discovery rate [FDR] < 0.05) and subsequent RT-qPCR verified that genes that are critical for osteogenesis, including Alpl, Bglap, Col1a1, Spp1, Sp7, and Runx2, were affected by the ablation of osteocytes (Figure 4—figure supplement 1B and C). Also, gene set enrichment analysis (GSEA) revealed that osteogenesis-related pathways, including Wnt signaling pathway, Hedgehog signaling pathway, and Notch signaling pathway, were downregulated (Figure 4—figure supplement 1D–F). In addition, the number of osteoblasts (N.Ob/BS) and osteoid-covered surface (OS/BS) was remarkably reduced in DTAhet mice compared to WT mice (Figure 4A and B). Also, bone marrow fat accumulation in DTAhet mice was observed (Figure 4C and D). Together, these results suggested that DTAhet mice displayed increased adipogenesis and decreased osteogenesis. To further evaluate the dynamics of bone formation in DTAhet mice, a 7-day dynamic histomorphometric analysis using calcein labeling was performed. The result showed that mineral surface (MS/BS), mineral apposition rate (MAR), and bone formation rate (BFR/BS) were significantly decreased in DTAhet mice (Figure 4E and F). Serum procollagen type 1 N-terminal propeptide (P1NP), a bone formation index, was also reduced after osteocyte ablation (Figure 4G). Intriguingly, in vitro osteogenesis showed that there were also decreased osteogenesis and mineralization in DTAhet mice compared to WT mice at both time points of 4 and 13 weeks and the impairment of osteogenesis was greater in DTAhet mice at 13 weeks compared to 4 weeks (Figure 4H and I). And the mRNA level of osteogenic markers at 4 weeks, including Alpl, Bglap, and Runx2, was also decreased (Figure 4J).

Figure 4 with 1 supplement see all
Ablation of osteocytes alters mesenchymal lineage commitment and promotes osteoclastogensis.

(A, B) Goldner trichrome staining of male WT and DTAhet mice femur at 4 weeks (A) and histomorphometry analysis of osteoblast numbers (N.Ob/BS) (arrows) and osteoid-covered surface (OS/BS) (B) (n = 6 per group). Scale bar, 20 µm. (C, D) Hematoxylin–eosin staining of WT and DTAhet mice femur at 4 weeks (C) and histomorphometry analysis of adipocyte (arrows) volume (Ad.V/TV) (D) (n = 6 per group). Scale bar, 50 µm. (E, F) Representative images of calcein double labeling of the mineral layers of male WT and DTAhet mice femur at 4 weeks (E) and histomorphometry analysis of the mineral surface (MS/BS), mineral apposition rate (MAR), and bone formation rate (BFR/BS) (F) (n = 4 per group). Scale bar, 50 µm. (G) ELISA of the concentration of bone formation index P1NP in the serum (n = 6–7 per group). (H, I) Alizarin red staining of osteogenesis from 4-week (H) and 13-week mice (I). Scale bar, 250 µm. (J) RT-qPCR analysis of osteoblast signature genes expression at the mRNA levels of osteogenesis from 4-week mice (n = 3 per group from three independent experiments), indicating impaired osteogenesis in DTAhet mice. (K, L) Tartrate-resistant acid phosphatase (TRAP) staining of WT and DTAhet mice femur at 4 weeks (K) and histomorphometry analysis of osteoclast (arrows) surface (Oc.S/BS) and osteoclast numbers (N.Oc/BS) (L) (n = 6 per group). Scale bar, 20 µm. (M) ELISAs of the concentration of receptor activation of nuclear factor-κ B ligand (RANKL), osteoprotegrin (OPG), and the ratio of RANKL/OPG in the serum (n = 6–7 per group). (N) ELISA of the concentration of bone resorption index CTX in the serum (n = 6–7 per group). (O, P) TRAP staining of osteoclastogenesis from 4-week (O) and 13-week mice (P) and quantitative analysis (Q) of TRAP-positive cells (nucleus > 3) per well (n = 3 per group from three independent experiments). Scale bar, 250 µm. (R) RT-qPCR analysis of osteoclast signature genes expression at the mRNA level of osteoclastogenesis from 4-week mice (n = 3 per group from three independent experiments), showing increased osteoclastogensis in DTAhet mice. Error bar represents the standard deviation.

Figure 4—source data 1

Ablation of osteocytes alters mesenchymal lineage commitment and promotes osteoclastogensis.

https://cdn.elifesciences.org/articles/81480/elife-81480-fig4-data1-v2.xlsx

In the aspect of osteoclastogenesis, histomorphometry analysis revealed that osteoclast surface (Oc.S/BS) and numbers (N.Oc/BS) were significantly increased after osteocytes deletion (Figure 4K and L). Circulatory RANKL was also increased in DTAhet mice (Figure 4M). In contrast, circulatory osteoprotegrin (OPG), a decoy receptor of RANKL, was decreased (Figure 4M), leading to the elevated ratio of RANKL/OPG (Figure 4M). Serum collagen type I c-telopeptide (CTX), a bone resorption index, was also significantly augmented in DTAhet mice compared to WT mice (Figure 4N), which implicated a high level of osteoclast activity of DTAhet mice in vivo. Also, flow cytometry analysis revealed that there was a slight increase (less than 1%) of osteoclast progenitors (B220-CD11bloLy-6Chi) in DTAhet mice at 4 weeks compared to WT mice (Figure 4—figure supplement 1G and H). To assess the effects of osteocyte ablation on osteoclastogenesis, bone marrow-derived macrophages (BMMs) from WT and DTAhet mice at both time points of 4 and 13 weeks were collected and plated at the same density for the examination of osteoclastogenesis in vitro. The results showed that osteoclastogenesis was increased in DTAhet mice compared to WT mice at both time points (Figure 4O and Q). Interestingly, the induction of osteoclastogenesis was greater in DTAhet mice at 13 weeks compared to 4 weeks (Figure 4P and Q), suggesting the time-dependent accumulative effect of osteoclastogenesis in DTAhet mice. Also, the expression of the signature genes of osteoclasts, including Acp5, Calcr, and Ocstamp, at the mRNA level was significantly upregulated in DTAhet mice (Figure 4Q). Together, osteocytes ablation impaired osteogenesis and promoted osteoclastogenesis.

Alteration of hematopoietic lineage commitment by osteocyte ablation

As a part of the skeletal system, bone marrow has its vital functions in maintaining bone homeostasis (Divieti Pajevic and Krause, 2019; Fulzele et al., 2013; Asada et al., 2013). HSCs give rise to lymphoid and myeloid lineage cells to establish the hematopoietic and immune system. To gain a full insight into the role of osteocyte in bone marrow homeostasis, single-cell RNA sequencing (scRNA-seq) was performed using 10X Genomics Chromium platform. After rigorous quality control, gene expression data from 26,562 cells (13,835 and 12,727 cells from 4-week littermate WT and DTAhet mice, respectively) were compiled for clustering analysis and revealed 10 distinct populations visualized with Uniform Manifold Approximation and Projection (UMAP) embeddings (Figure 5A–C). These 10 distinct populations included B cell, hematopoietic stem cell and progenitor cell (HSPC), megakaryocyte, neutrophil, erythrocyte, monocyte, dendritic cell (DC), macrophage, T cell, and MSC (Figure 5A and C). Proportion analysis revealed a significant expansion of neutrophils in DTAhet mice (Figure 5D and E). Also, the number of B cells was significantly less in DTAhet mice than that in WT mice (Figure 5D and E), which implicated that osteocytes ablation induced lymphoid–myeloid malfunction in the bone marrow. To further dissect the differences in the bone marrow development between the two groups, RNA velocity was performed. The result showed that DTAhet mice had stronger directionality of velocity vectors from the HSPC population to the neutrophil population compared to WT mice (Figure 5F), implying that osteocytes deletion altered HSPC differentiation. Meanwhile, myeloid trajectory analysis revealed that there was a significantly higher pseudotime density distribution in G4 cell (a subcluster of neutrophil) in DTAhet mice (Figure 5G). In contrast, lymphoid trajectory analysis demonstrated a relatively lower pseudotime density distribution in pre-B cell and immature B cell (subclusters of B cell) in DTAhet mice (Figure 5H).

Figure 5 with 3 supplements see all
Alteration of hematopoietic lineage commitment by osteocyte ablation.

(A, B) The Uniform Manifold Approximation and Projection (UMAP) plot of cells isolated from the bone marrow of 4-week WT and DTAhet mice and inferred cluster identity (A) and number of mRNA per cell (B). (C) Dot plot showing the scaled expression of selected signature genes for each cluster. Dot size represents the percentage of cells in each cluster with more than one read of the corresponding gene, and dots are colored by the average expression of each gene in each cluster. (D, E) The UMAP plot of cells shown by sample (D) and proportions of each cluster in two samples (E). (F) RNA velocity analysis of clusters of WT and DTAhet mice shown by the UMAP embedding, showing stronger directionality of velocity vectors from hematopoietic stem cell and progenitor cell (HSPC) cluster to neutrophil cluster in DTAhet mice. (G) Trajectory analysis of myeloid clusters of WT and DTAhet mice, demonstrating myeloid-biased hematopoiesis in DTAhet mice. (H) Trajectory analysis of lymphoid clusters of WT and DTAhet mice, demonstrating impaired lymphopoiesis in DTAhet mice. (I, J) Representative image of flow cytometry (I) and analysis of proportions of myeloid progenitors (common myeloid progenitors [CMP], granulocyte–monocyte progenitors [GMP], and megakaryocyte erythroid progenitors [MEP]) (J) of 4-week WT and DTAhet mice (n = 3–4 per group). (K, L) Representative image of flow cytometry (K) and analysis of proportions of CD11b+ myeloid cells (L) of 4-week WT and DTAhet mice (n = 3 per group). (M, N) Representative image of flow cytometry (M) and analysis of proportions of neutrophils (N) of 4-week WT and DTAhet mice (n = 3–4 per group). (O, P) Representative image of flow cytometry (O) and analysis of proportions of B220+ lymphoid cells (P) of 4-week WT and DTAhet mice (n = 3 per group). (Q, R) Representative image of flow cytometry (Q) and analysis of proportions of ProB PreB, immature B, transitional B, early mature B, and late mature B (R) of 4-week WT and DTAhet mice (n = 3–4 per group), indicating altered B cell development pattern in DTAhet mice. Error bar represents the standard deviation.

Figure 5—source data 1

Alteration of hematopoietic lineage commitment by osteocyte ablation.

https://cdn.elifesciences.org/articles/81480/elife-81480-fig5-data1-v2.xlsx

To corroborate the results observed from scRNA-seq, flow cytometry and further analysis were performed after removing adherent cells as previously reported (Ding et al., 2022; Figure 5—figure supplement 1A and B). Although there was no significant change in HSC (Lin-c-Kit+Sca1+, LSK+ cell) numbers between DTAhet and WT mice (Figure 5—figure supplement 2A and B), DTAhet mice demonstrated significantly increased number of short-term HSCs (ST-HSCs) with decreased number of long-term HSCs (LT-HSCs), indicating that HSCs in DTAhet mice bone marrow were mobilized (Figure 5—figure supplement 2C and D). Further flow cytometry analysis revealed that the number of myeloid progenitors, including common myeloid progenitors (CMP), granulocyte–monocyte progenitors (GMP), and common monocyte progenitors (cMoP), was substantially increased after osteocyte ablation (Figure 5I and J, Figure 5—figure supplement 2E and F), and megakaryocyte erythroid progenitors (MEP) numbers were decreased (Figure 5I and J). Meanwhile, total CD11b+ myeloid cells were also increased (Figure 5K and L) in DTAhet mice, in which both neutrophils and monocytes significantly expanded (Figure 5M and N, Figure 5—figure supplement 2G and H). In addition, while the proportion of common lymphoid progenitors (CLP) was not altered in DTAhet mice (Figure 5—figure supplement 2I and J), total B220+ lymphoid cells reduced remarkably after osteocyte ablation (Figure 5O and P), in which DTAhet mice showed a relatively lower proportion of early B cell (pro-B pre-B, immature B, and transitional B cell) and a relatively higher proportion of late B cell (early mature B and late mature B) (Figure 5Q and R), which suggested that B cell development was impaired along the immature B to mature B cell transition in DTAhet mice. As scRNA-seq revealed that neutrophils underwent a significant change after osteocyte ablation, neutrophil population were further reclustered into four subclusters from G1 to G4 (Figure 5—figure supplement 3A and B) and G4 population was significantly increased in DTAhet mice compared to WT mice (Figure 5—figure supplement 3C and D), which implied that osteocyte ablation accelerated neutrophil maturation. Consistent with this observation, neutrophil functions, including activation and chemotaxis, were all upregulated in DTAhet mice (Figure 5—figure supplement 3E and F). Genes related to glycolysis and necroptosis were also upregulated (Figure 5—figure supplement 3G and H), indicating that osteocyte ablation altered neutrophil functions. Together, these results demonstrated that osteocyte ablation altered hematopoietic lineage, characterized by the shift from lymphopoiesis to myelopoiesis.

Senescence of osteoprogenitors and myeloid lineage cells leads to the accelerated skeletal aging

Senescence occurred during development as a precise programmed cellular process, contributing to cell fate specification, tissue patterning, and transient structure removal (Muñoz-Espín and Serrano, 2014; Rhinn et al., 2019). Given that DTAhet mice had accelerated skeletal phenotype of aging, including increased myelopoiesis, osteoporosis, kyphosis, and sarcopenia with shortened lifespan, we hypothesized that osteocyte ablation may be associated with senescence of osteoprogenitors and myeloid lineage cells. ScRNA-seq revealed that total bone marrow had increased senescence with a higher SASP score in DTAhet mice compared to WT mice (Figure 6A). DTAhet mice also had increased maturity in bone marrow reflected from RNA velocity (Figure 6B). Meanwhile, circulatory SASP index, including TNF-α, IL-1β, and IL-6, were also elevated in DTAhet mice (Figure 6C). Further scRNA-seq analysis uncovered that MSC, CMP, monocyte, and its subcluster Ly6c2_monocyte, neutrophil, and its subcluster G2, G3, and G4, had increased SASP scores (Figure 6D–G) and higher-level expressions of senescence-related genes in DTAhet mice (Figure 6H). RT-qPCR also verified the elevated senescence with increased gene expressions, including Cdkn2a and Cdkn1a in DTAhet mice (Figure 6I and J). Further, senescence-associated β-galactosidase (SA-βGal) staining revealed that there were obvious increased numbers of SA-βGal+ cell in the primary spongiosa, bone marrow, and cortical bone in DTAhet mice compared to WT mice (Figure 6—figure supplement 1A). Together, these results suggested that cell senescence of osteoprogenitors and myeloid lineage cells was associated with ablation of osteocyte.

Figure 6 with 1 supplement see all
Senescence of osteoprogenitors and myeloid lineage cells leads to the accelerated skeletal aging.

(A) Comparisons of total bone marrow cells senescence-associated secretory phenotype (SASP) score between 4-week WT and DTAhet mice. (B) Latent time of RNA velocity analysis of WT and DTAhet mice shown by the Uniform Manifold Approximation and Projection (UMAP) embedding. (C) ELISAs of the concentration of TNF-α, IL-1β, and IL-6 of 4-week WT and DTAhet mice in the serum (n = 5–6 per group). (D) Comparisons of mesenchymal stem cells (MSCs) SASP score between 4-week WT and DTAhet mice, indicating the senescence of osteoprogenitors in DTAhet mice. (E) Comparisons of common myeloid progenitors (CMP) SASP score between 4-week WT and DTAhet mice. (F) Comparisons of monocytes and its subcluster Ly6c2_monocytes SASP score between 4-week WT and DTAhet mice. (G) Comparisons of neutrophils and its subcluster (G2, G3, and G4) SASP score between 4-week WT and DTAhet mice, indicating the senescence of myeloid lineage cells. (H) Bubble plot of the expression of senescence-related genes in subclusters of WT and DTAhet mice. (I) RT-qPCR analysis of senescence-related genes expression at the mRNA level of 4-week WT and DTAhet mice cortical bone (n = 3 per group). (J) RT-qPCR analysis of senescence-related genes expression at the mRNA level of 4-week WT and DTAhet mice bone marrow (n = 3 per group). Error bar represents the standard deviation.

Figure 6—source data 1

Senescence of osteoprogenitors and myeloid lineage cells leads to the accelerated skeletal aging.

https://cdn.elifesciences.org/articles/81480/elife-81480-fig6-data1-v2.xlsx

Owning to the fact that osteoblast derives from MSC lineage, we next investigated whether accumulation of osteoprogenitor cell senescence impaired osteogenesis. GO analysis revealed that downregulated genes after osteocyte ablation were enriched in ossification and biomineral tissue development (Figure 6—figure supplement 1B), which was consistent with the finding of impaired osteoblast differentiation (Figure 4H–J). Meanwhile, the mRNA level of adipogenic markers, including Adipoq, Fabp4, Pparg, and Cebpa, was significantly increased (Figure 6—figure supplement 1C), indicating increased adipogenesis and alteration of MSC commitment after osteocyte ablation. In addition, the mRNA levels of cartilage anabolism-related genes (Col1a2, Acan, Sox9, and Prg4) and catabolism-related genes (Mmp3, Mmp13, Adamts1, and Adamts5) were not significantly changed (Figure 6—figure supplement 1D), indicating that chondrogenesis was not altered after osteocyte ablation. Similarly, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the subclusters 2 and 4 of Ly6c2+ monocytes demonstrated the enrichment of osteoclast differentiation-related genes after osteocyte ablation (Figure 6—figure supplement 1E and F), which was corroborated in our enhanced in vitro osteoclast differentiation (Figure 4O–R). Together, our data suggested that senescence in osteoprogenitors and myeloid lineage cells led to the impaired osteogenesis and increased osteoclastogenesis, respectively.

Discussion

In this study, we showed that coordination of bone and bone marrow homeostasis requires the presence of functional osteocytes. Reduction of osteocytes number results in the detrimental impact of lineage cell fate and specifications in bone and bone marrow. Partial ablation of osteocytesDMP1 caused severe sarcopenia, osteoporosis, and degenerative kyphosis, which led to shorter lifespan. Acquisition of SASP in both osteogenic and myeloid lineage cells may be an underlying cause that led to the accelerated skeletal aging phenotype of impaired osteogenesis, increased osteoclastogenesis, and myelopoiesis.

Sarcopenia usually occurs concurrently with osteoporosis during aging (Clynes et al., 2021). Our study has shown for the first time that osteocyte ablation caused severe sarcopenia and muscle atrophy. Consistent with our observation, previous studies have reported that osteocyte-specific ablation of Cx43 impaired muscle formation (Shen et al., 2015). Osteocyte-derived factors have also been shown to stimulate myogenic differentiation in vitro (Huang et al., 2017). On the contrary, specific deletion of Mbtps1 in osteocyte promotes soleus muscle regeneration and increase its size with age (Gorski et al., 2016). Sclerostin, an osteocyte-derived circulating protein, is negatively correlated with skeletal muscle mass (Kim et al., 2019). Previously there has been a study showing weak Dmp1 expression in skeletal muscle fibers (Lim et al., 2017). This has led us to suggest that sarcopenia may be caused directly by the Dmp1 expression in muscle. However, our histology finding of no obvious changes in the total number of nuclei of muscle in partial ablation of DMP1-positive osteocytes suggested that the sarcopenia and muscle atrophy phenotype is most likely caused by the disturbance of osteocyte-muscle crosstalk. Certainly, further studies based on a more specific osteocyte ablation model are needed to understand the link of osteocytes between osteoporosis and sarcopenia. Nevertheless, severe kyphosis observed in these osteocyte ablation mice supports our hypothesis of direct osteocyte-muscle crosstalk as kyphosis is the direct result of the significant bone loss and sarcopenia (Wijshake et al., 2012; Woods et al., 2020).

Osteocytes regulate the process of bone resorption and coupled bone formation via secreting factors, including sclerostin and RANKL (Tresguerres et al., 2020; van Bezooijen et al., 2005; Nakashima et al., 2011). Theoretically, osteocyte ablation may lead to lower expression of sclerostin and RNAKL, which in term increased osteogenesis and impaired osteoclastogenesis. However, our results demonstrated that osteocyte ablation impaired osteogenesis and induced osteoclastogenesis. In mice with partial ablation of osteocytes, expression of sclerostin was reduced but the serum RNAKL was increased. In addition, osteogenesis-related pathways, including Wnt signaling pathway, Hedgehog signaling pathway, and Notch signaling pathway, were also downregulated. We speculated that induction of SASP in both osteoprogenitors and myeloid progenitors may account for the underlying cause. Senescent osteoprogenitors have reduced self-renewal capacity and predominantly differentiate into adipocytes as opposed to osteoblasts (Chen et al., 2016; Li et al., 2017; Rosen et al., 2009). Consistently, our model indicated an increased adipogenesis after osteocyte ablation. Also, fat-induction factors inhibit osteogenesis during adipogenesis (Chen et al., 2016). Thus, senescence accumulation in osteoprogenitors led to the impaired osteogenesis. As for enhanced osteoclastogenesis, besides the production of RANKL from osteogenic cells like osteocytes and osteoblasts (Nakashima et al., 2011; Fumoto et al., 2014), other cells like adipocyte and T cell, also secret RANKL to regulate bone metabolism (Yu et al., 2021; Hu et al., 2021; Djaafar et al., 2010; Takayanagi et al., 2000). Also, B cell can produce OPG to regulate RANKL/OPG axis (Li et al., 2007). In our model, increased adipogenesis, T cell expansion (data not shown), and decreased B cell number may compensate for the altered RANKL/OPG axis. Intriguingly, we also found that even under in vitro condition in which osteocyte ablation no longer exists, impairment of osteogenesis and induction of osteoclastogenesis were still observed. Our study has suggested that osteoprogenitors and BMMs have been primed by the altered bone microenvironment in DTAhet mice before in vitro differentiation. In support of this, previous studies have suggested that progenitor cells can receive a long-lasting impact from the in vivo local microenvironment, where these cells are situated. Isolation of cells for in vitro cell differentiation or even transferring cells to healthy mice would not alter their original in vivo phenotypes (Cao et al., 2020; Isaac et al., 2014; Ding et al., 2022; Edgar et al., 2021; Li et al., 2022).

Bone marrow, embedded in the skeletal system, has a close link with matrix-embedded osteocyte. Previous studies have reported that osteocyte regulates myelopoiesis via Gsα-dependent and -independent signaling (Fulzele et al., 2013; Azab et al., 2020). A recent study also reported that osteocyte mTORC1 signaling regulates granulopoiesis via secreted IL-19 (Xiao et al., 2021). Meanwhile, sclerostin secreted by osteocyte adversely affects B cell survival (Horowitz and Fretz, 2012). In our study, when osteocytes were partially depleted, myelopoiesis, especially granulopoiesis, was significantly induced, but B cell development was significantly impaired. Further studies demonstrated that HSCs were mobilized and shifted to myelopoiesis with increased CMP, GMP, cMoP, and CD11b+ myeloid cells, in which monocytes and neutrophils were increased, and neutrophil function was also activated after osteocyte ablation. While B cell number was severely reduced with altered development pattern. Interestingly, a previous study has shown that osteoblastic cell supports megakaryopoiesis and platelet formation (Xiao et al., 2017). In our study, the number of MEP (erythrocyte and platelet precursors) was also reduced, and scRNA-seq analysis showed no significant change in erythrocyte population (data not shown), inferring that osteocyte may also participate in regulating platelet formation.

Bone and bone marrow harbor different cell lineages and form specific niches to maintain complex, delicate and extensive communications between them (Hu et al., 2016). Previous studies have shown that osteocyte controls bone remodeling, regulates hematopoiesis, and even remote organ function (Divieti Pajevic and Krause, 2019; Asada et al., 2015), via secretion of factors, including sclerostin, RANKL, FGF23, and IL19 (Xiao et al., 2021). Although our study has shown that osteocytes also influence cell lineage commitments of bone and bone marrow via the mediation of cell senescence, it is still not clear what factors osteocytes produce to regulate this process. Further study is required to identify the mechanism in which osteocytes regulate the homeostasis of bone and bone marrow. Furthermore, as our study only focuses on the effect of osteocyte ablation in muscle, bone, and bone marrow, it is still not clear what is the impact of osteocyte ablation on other organs. Nevertheless, a previous study showed that osteocyte ablation induces lymphoid organ atrophy, thymocyte depletion, and altered fat metabolism in ‘osteocyte-less’ mice model (Sato et al., 2013), suggesting the role of osteocytes in the extraskeletal system.

In conclusion, we demonstrated a critical role of osteocytes in regulating senescence of bone and bone marrow (Figure 7). Ablation of osteocytes induced SASP accumulation in bone marrow osteoprogenitors and myeloid lineage cells, which altered MSC and HSC lineage commitments with impaired osteogenesis, and promoted myelopoiesis and osteoclastogenesis, leading to the accelerated skeletal aging phenotype with severe sarcopenia, osteoporosis, degenerative kyphosis, and bone marrow myelopoiesis, thus shortened lifespan of mice. Targeting osteocyte function and cell fate may shed light on the therapeutic regimens for aging-associated bone diseases.

Schematic diagram of osteocyte ablation-induced skeletal senescence.

Ablation of osteocytes induced senescence-associated secretory phenotype (SASP) accumulation in bone marrow osteoprogenitors and myeloid lineage cells, which altered mesenchymal stem cell (MSC) and hematopoietic stem cell (HSC) lineage commitments with promoted adipogenesis, myelopoiesis, and osteoclastogenesis at the expense of osteogenesis and lymphopoiesis, leading to the accelerated skeletal aging phenotype with severe sarcopenia, osteoporosis, degenerative kyphosis, and bone marrow myelopoiesis, thus shortened lifespan of mice.

Materials and methods

Mice

All mouse lines were maintained on a C57BL/6J background. Dmp1cre mice were provided by J. Q. (Jerry) Feng from Texas A&M College of Dentistry, USA (Jackson Laboratory stock number: 023047). Rosa26em1Cin(SA-IRES-Loxp-ZsGreen-stop-Loxp-DTA) heterozygotes were from GemPharmatech (strain ID: T009408). Osteocyte ablation mice model during development was established by crossing Dmp1cre mice with Rosa26em1Cin(SA-IRES-Loxp-ZsGreen-stop-Loxp-DTA) homozygotes to obtain Dmp1cre Rosa26em1Cin(SA-IRES-Loxp-ZsGreen-stop-Loxp-DTA) heterozygotes (DTAhet). All mice experiments were approved by the Animal Care and Use Committee of Shanghai Sixth People’s Hospital (permit number: 2021-0935, 2021-0936). All surgeries were performed under anesthesia using isoflurane or sodium pentobarbital, and every effort was made to minimize suffering.

Bone histomorphometry analysis

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Mice femur was dissected and fixed in 4% paraformaldehyde (PFA) for 2 days and further decalcified with 10% EDTA (pH = 7.2) at 4℃ for about 2 weeks. Then, specimens were embedded in paraffin and sectioned at 4 μm thickness. TRAP staining was performed for osteoclast analysis. Hematoxylin–eosin (H&E) staining was performed for adipocyte and osteocyte analysis. For osteoblast analysis, undecalcified femur was embedded in plastic and sectioned at 5 μm thickness and Goldner trichrome staining was performed. For dynamic histomorphometry analysis, double calcein labeling was used. Briefly, each mouse was given 30 μg/g body weight calcein (Sigma) on days 1 and 7 by intraperitoneal injection before sacrifice. Bones were then fixed, dehydrated, embedded in plastic, cut into 5 μm slices, and calculated using the software under fluorescence. BioQuant Osteo software (BioQuant) was used for histomorphometry analysis. Accepted nomenclature was used to report the results (Dempster et al., 2013). ImageJ was used to measure the number of osteocyte lacunae.

Immunofluorescence staining

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Both ends of the mice’s tibias/femurs were removed. Then, they were embedded in OCT for frozen sectioning and cut parallel to the long axis of the long bones. Stop cutting when the maximum cross section of the long bones was observed. The OCT around the rest of the bones were melted at room temperature. The remaining bone samples were washed three times in PBS for 10 min and fixed in 4% PFA for 2 hr. Then, they were immersed in 0.1% Triton X-100 for 1 hr, blocked using 3% BSA, and stained using Alexa Fluor 568 Phalloidin (Invitrogen) for 48 hr at 4°C in the dark with gentle shake. The samples were washed three times with PBS for 10 min. The cross section of the sample was inverted in the confocal dish. Pictures were captured using confocal microscopy (Olympus), and ImageJ was used to measure the number of dendrites per osteocyte.

SA-βGal staining

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For SA-βGal staining, mice femur was dissected and fixed in 4% PFA for 2 days and further decalcified with 10% EDTA (pH = 7.2) at 4°C for about 2 weeks. Then, specimens were dehydrated in 30% sucrose and embedded in OCT and frozen sectioned at 10 μm thickness. Then, SA-βGal staining was performed according to the manufacturer’s instructions (Beyotime).

Bone density measurements

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Mice femurs and L3 lumbar were stripped of soft tissue and fixed in 4% PFA overnight at 4°C, then stored in 70% ethanol until scanned using the μCT instrument (SkyScan 1176). Relevant structure parameters of the μCT instrument were as previously reported (Ding et al., 2022): scanning voxel size, 9 × 9 × 9 µm3; X-ray tube potential, 50 kV and 450 μA; integration time, 520 ms; and rotation step, 0.4° for 180° scanning. CTAn micro-CT software version 1.13 (Bruker) was used to analyze the images. The threshold value (grayscale index) for all trabecular bone was 75. For all cortical bones, the threshold value (grayscale index) was 110. The femurs were analyzed at a resolution of 9 μm. The volumetric regions for trabecular analyses include the secondary spongiosa located 1 mm from the growth plate and extending 1.8 mm (200 sections) proximally. For cortical bone analysis, the volumetric regions include 600 μm long at mid-diaphysis of the femur (300 μm extending proximally and distally from the diaphyseal midpoint between the proximal and distal growth plates). For vertebrae, the volumetric regions include the entire trabecular region without the primary spongiosa (300 μm below the cranial and above the caudal growth plate). Morphometric parameters, including BMD, BV/TV, Tb.N, Tb.Th, Tb.Sp, Ct.Th, and Ct.Po, were calculated.

Gait analysis

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CatWalk automated gait analysis system (Noldus Information Technology) was used to analyze gait. Mice were expected to run along a special glass plate with a green LED lit and a high-speed video camera under it. Their paws were captured by the camera. Before the formal experiments, the mice were habituated in the plate to achieve an unforced locomotion. Three compliant runs without stopping, changing direction, and turning around were analyzed with CatWalk Software. Relevant data were generated by CatWalk Software after each footprint was checked manually. Data including stride length, swing speed, and normal step sequence radio were analyzed.

Whole-mount Alcian blue/Alizarin red staining

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The skin and viscera of the intact fetal mice (E19.0) were removed. The embryos were fixed in 95% ethanol overnight and then degreased in absolute acetone overnight with gentle agitation. The embryos were stained overnight in 0.015% Alcian blue (Sigma)/0.005% Alizarin red (Sigma) in 70% ethanol with gentle agitation. They were washed in 70% ethanol for 30 min three times and digested using 1% KOH solution. When most of the soft tissue was digested, the embryos were immersed in 75% (vol/vol)/1% KOH/glycerol solution for further clearing. Graded glycerol was changed according to the degree of embryos digestion and relevant pictures were obtained under the microscope (Leica).

Whole-body µCT scan

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The 13- and 37-week-old DTAhet and WT mice were deeply anesthetized and carefully positioned with a dedicated cradle and holder to capture the whole-body (excluding the tail) radiographs at a resolution of 35 µm using the μCT instrument (SkyScan 1176). Scanning details are listed as follows: X-ray tube potential, 65 kV and 375 μA; exposure time, 150 ms; and rotation step, 0.5° for 180° scanning. CTAn micro-CT software version 1.13 (Bruker) was used to reconstruct pictures.

RNA-seq

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Total RNA of whole bone with bone marrow flushed out from 4-week WT and DTAhet mice was extracted using TRIzol reagent (Thermo Fisher), quantified and purified using Bioanalyzer 2100 and RNA 6000 Nano LabChip Kit (Agilent). Following purification, mRNA library was constructed, fragmented, amplified, and loaded into the nanoarray and sequencing was performed on Illumina NovaSeq 6000 platform following the vendor’s recommended protocol. After sequencing, generated reads were filtered and mapped to the reference genome using HISAT2 (v2.0.4) and assembled using StringTie (v1.3.4d) with default parameters. Then, all transcriptomes from all samples were merged to reconstruct a comprehensive transcriptome using GffCompare software (v0.9.8), and the expression levels of all transcripts were calculated by Stringtie and ballgown. Differential gene analysis was performed by DESeq2 software and then subjected to enrichment analysis of GO functions. GSEA was performed using GSEA software (version 4.1.0; Broad Institute, MIT). Genes were ranked according to their expression; gene sets were searched from website (https://www.gsea-msigdb.org). The data were deposited into the GEO repository (GSE202356).

Cell culture

In vitro osteoclastogenesis assay

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The bone marrow of mice femurs and tibias were flushed to get bone marrow cells. Cells were cultured overnight by using α-MEM (Hyclone), which contains 10% FBS (Gibco), 100 μg/ml streptomycin (Gibco), and 100 U/ml penicillin (Gibco). The nonadherent cells were collected, layered on Ficoll-Paque (GE Healthcare), and separated through density gradient centrifugation at 4°C and 2000 rpm for 20 min. The BMMs in the middle layer of the separation were collected and washed twice with ice-cold PBS. To induce osteoclast differentiation, BMMs (2.5 × 104 cells per well for 96-well plates and 8 × 105 per well for 6-well plates) were cultured by using α-MEM, which contains 10% FBS, 100 μg/ml streptomycin, 100 U/ml penicillin, 100 ng/ml M-CSF (PeproTech), and 100 ng/ml RANKL (PeproTech) for 5 days before TRAP staining. Cells were cultured at 37°C in a humidified incubator at 5% CO2. The medium was changed every 2 days. At the end of assay (the fifth day), the cells were fixed and stained with tartrate-resistant acid phosphatase (TRAP) kit according to the manufacturer’s instructions (Sigma) to quantify osteoclast numbers, or RNA was extracted as per the recommended protocol. TRAP-positive cells that contain more than three nuclei were counted as mature osteoclast-like cells (OCLs). The assay was repeated three times, and the number of OCLs per well was recorded for each biological replicate.

Isolation of mesenchymal stem cells and trilineage differentiation

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Bone marrow cells were collected by flushing femur and tibia from WT and DTAhet mice and were cultured in DMEM (Hyclone) containing 10% FBS, 100 U/ml penicillin, and 100 μg/ml streptomycin. After 48 hr, nonadherent cells were removed and fresh medium was added. The adherent spindle-shaped cells were further cultured for 2 days. After culturing the cells to 70–80% confluence, they were replated at a density of 5000 cells per well for 96-well plates or 2 × 105 cells per well for 6-well plates. When the cells were cultured to 70–80% confluence, the medium was replaced with osteogenic differentiation medium (Cyagen) for osteogenesis or with adipogenic differentiation medium (Cyagen) for adipogenesis or with chondrogenic differentiation medium (Cyagen). RNA extraction was performed after 2 days of differentiation. After 3 weeks of differentiation of osteogenesis, Alizarin red staining was performed.

RT-qPCR

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Total RNA was isolated using RNeasy Mini Kit (QIAGEN). 500 ng of total RNA was reverse-transcribed into cDNA using PrimeScript RT Master Mix (Takara, RR036A). qPCR analyses were performed using SYBR Premix Ex Taq Ⅱ (Takara, RR820L) and samples were run on the ABI HT7900 platform (Applied Biosystems). SYBR Green PCR conditions were 1 cycle of 95°C for 30 s, and 40 cycles of 95°C for 5 s and 34°C for 60 s. Melting curve stage was added to check primers’ specificity. Relative gene expression levels were calculated using the threshold cycle (2–ΔΔCT) method. Relevant primers are listed as follows: Gapdh: 5′-ACC CAG AAG ACT GTG GAT GG-3′ and 5′-CAC ATT GGG GGT AGG AAC AC-3′; Cdkn1a: 5′-GTC AGG CTG GTC TGC CTC CG-3′ and 5′-CGG TCC CGT GGA CAG TGA GCA G-3′; Cdkn2a: 5′-GTC AGG CTG GTC TGC CTC CG-3′ and 5′-CGG TCC CGT GGA CAG TGA GCA G-3′; Ccl2: 5′-GCA TCC ACG TGT TGG CTC A-3′ and 5′-CTC CAG CCT ACT CAT TGG GAT CA-3′; Tnf: 5′-ATG AGA AGT TCC CAA ATG GC-3′ and 5′-CTC CAC TTG GTG GTT TGC TA-3′; Il1b: 5′-GCC CAT CCT CTG TGA CTC AT-3′ and 5′-AGG CCA CAG GTA TTT TGT CG-3′; Alpl: 5′-TCA GGG CAA TGA GGT CAC AT-3′ and 5′-CCT CTG GTG GCA TCT CGT TA-3′; Bglap: 5′-CCC TGA GTC TGA CAA AGC CT-3′ and 5′-GCG GTC TTC AAG CCA TAC TG-3′; Col1a1: 5′-ATA AGT CCC TTC CTG CCC AC-3′ and 5′-TGG GAC ATT TCA GCA TTG CC-3′; Spp1: 5′-ATG CCA CAG ATG AGG ACC TC-3′ and 5′-CCT GGC TCT CTT TGG AAT GC-3′; Sp7: 5′-TCG GGG AAG AAG AAG CCA AT-3′ and 5′-CAA TAG GAG AGA GCG AGG GG-3′; Runx2: 5′-GCC CAG GCG TAT TTC AGA TG-3′ and 5′-GGT AAA GGT GGC TGG GTA GT-3′; Dmp1: 5′-CAG TGA GGA TGA GGC AGA CA-3′ and 5′-CGA TCG CTC CTG GTA CTC TC-3′; Sost: 5′-GCC GGA CCT ATA CAG GAC AA-3′ and 5′-CAC GTA GCC CAA CAT CAC AC-3′; Acp5: 5′-TGG ACA TGA CCA CAA CCT GCA GTA-3′and 5′-TCG CAC AGA GGG ATC CAT GAA GTT-3′; Calcr: 5′-AGC CAC AGC CTA TCA GCA CT-3′ and 5′-GAC CCA CAA GAG CCA GGT AA-3′; Ocstamp: 5′-TGG GCC TCC ATA TGA CCT CGA GTA G-3′ and 5′-TCA AAG GCT TGT AAA TTG GAG GAG T-3′; Atp6v0d2: 5′-ACA TGT CCA CTG GAA GCC CAG TAA-3′ and 5′-ATG AAC GTA TGA GGC CAG TGA GCA-3′; Tyrobp:5′-CTG GTG TAC TGG CTG GGA TT-3′ and 5′-CTG GTC TCT GAC CCT GAA GC-3′; Adipoq: 5′-GAC CTG GCC ACT TTC TCC TC-3′ and 5′-TCC TGA GCC CTT TTG GTG TC-3′; Fabp4: 5′-GAT GAA ATC ACC GCA GAC GAC A-3′ and 5′-ATT GTG GTC GAC TTT CCA TCC C-3′; Pparg: 5′-GGA AAG ACA ACG GAC AAA TCA C-3′ and 5′-TAC GGA TCG AAA CTG GCA C-3′; Cebpa: 5′-TGG ACA AGA ACA GCA ACG AG-3′ and 5′-TCA CTG GTC AAC TCC AGC AC-3′; Col1a2: 5′-GGG AAT GTC CTC TGC GAT GAC-3′ and 5′-GAA GGG GAT CTC GGG GTT G-3′; Acan: 5′-CCT GCT ACT TCA TCG ACC CC-3′ and 5′-AGA TGC TGT TGA CTC GAA CCT-3′; Sox9: 5′-CGG AAC AGA CTC ACA TCT CTC C-3′ and 5′-GCT TGC ACG TCG GTT TTG G-3′; Prg4: 5′-GGG TGG AAA ATA CTT CCC GTC-3′ and 5′-CAG GAC AGC ACT CCA TGT AGT-3′; Mmp3: 5′-ACA TGG AGA CTT TGT CCC TTT TG-3′ and 5′-TTG GCT GAG TGG TAG AGT CCC-3′; Mmp13: 5′-CTT CTT CTT GTT GAG CTG GAC TC-3′ and 5′-CTG TGG AGG TCA CTG TAG ACT-3′; Adamts1: 5′-CAT AAC AAT GCT GCT ATG TGC G-3′ and 5′-TGT CCG GCT GCA ACT TCA G-3′; Adamts5: 5′-GGA GCG AGG CCA TTT ACA AC-3′ and 5′-CGT AGA CAA GGT AGC CCA CTT T-3′. All these primers were synthesized by Sangon Biotech Company (Shanghai).

Flow cytometry

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Bone marrow cells were isolated by flushing the bone marrow of mice femurs and tibias with PBS and were dissociated into a single-cell suspension by gently filtering them through 70 μm nylon mesh. After red blood cells lysis, the isolated cells were blocked by anti-mouse CD16/32 antibody (BioLegend, 101302) for 15 min and stained with fluorescence-conjugated antibodies for 30 min at 4°C in the dark. Relevant antibodies are listed as follows and their catalog numbers are provided in the parentheses: anti-Ly-6C-Pacific Blue (128013), anti-Ly-6C-PE (128007), anti-Ly-6G-Pacific Blue (127611), anti-Ly-6G-PE/Cy7 (127617), anti-CD16/32-FITC (101305), anti-CD115-PE (135505), anti-CD117-PE (105808), anti-CD117-APC/Cy7 (105825), anti-CD45R-PE/Cy5 (103209), anti-CD45R-APC (103212), anti-Ly-6A/E-APC (108111), anti-Ly-6A/E-Alexa Fluor700 (108142), anti-CD34-PerCP/Cyannine5.5 (128607), anti-CD135-APC (135309), anti-lineage cocktail-Pacific Blue (133305), anti-CD127-PE (121111), anti-CD127-APC(135011), anti-CD11b-FITC (101205), and anti-CD24-Pacific Blue (101819). All these antibodies were purchased from BioLegend. Samples were analyzed using cytometer CytoFlex (Beckman Coulter) and FlowJo software version 10.4. A total of 50,000 events were collected for each sample.

Preparation of mice serum

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For serum collection, mice were anesthetized with isoflurane and blood samples were collected from the ophthalmic vein. Samples were then centrifuged at 5000 rpm for 5 min. Supernatants were transferred to a new tube and centrifuged at 5000 rpm for 5 min again. Supernatants were collected to a new tube and treated with liquid nitrogen fastly and then stored at –80°C.

Enzyme-linked immunosorbent assay (ELISA)

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ELISA was performed as per the manufacturer’s instructions (Jianglai). Briefly, working standards and diluted samples were prepared and added to each well. Plates were sealed and incubated for 1 hr at 37°C. After washing three times, 100 μl enzyme-labeled reagents were added and plates were incubated for 1 hr at 37°C. Finally, TMB substrates were added and incubated for 15–30 min at 37°C followed by Stop solution addition. Then, plates were read at 450 nm within 5 min.

Singe-cell collection, library construction, and sequencing

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Bone marrow cells from WT and DTAhet mice were flushed and sieved through a 70 µm cell strainer. After red blood cell analysis, dissociated single cells were stained with AO/PI for viability assessment. scRNA-seq was performed using 10X Genomics Chromium platform. Related operations, including generation of gel beads in emulsion (GEMs), barcoding, GEM-RT cleanup, complementary DNA amplification, and library construction, were all carried out following the manufacturer’s protocol. By using 150-base-pair paired-end reads, the final libraries were sequenced on the Illumina NovaSeq 6000 platform. The scRNA-seq data could be accessed from GEO database (GSE202516, secure token for reviewer: ihudckqqxvopruz).

Data processing, dimension reduction, unsupervised clustering, and annotation

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ScRNA-seq data analysis was performed by NovelBio Co., Ltd with NovelBrain Cloud Analysis Platform (https://www.novelbrain.com). Fastp was applied with default parameters filtering the adaptor sequence, and the low-quality reads were removed to achieve the clean data. Then, the feature-barcode matrices were obtained by aligning reads to the mouse genome (mm10 Ensemble: version 92) using CellRanger v3.1.0. Down-sample analysis among samples sequenced was applied according to the mapped barcoded reads per cell of each sample and finally the aggregated matrix was achieved. Cells contained over 200 expressed genes, mitochondria UMI rate below 20% passed the cell quality filtering, and mitochondria genes were removed in the expression table.

Seurat package (version 3.1.4; https://satijalab.org/seurat/) was used for cell normalization and regression based on the expression table according to the UMI counts of each sample and percent of mitochondria rate to obtain the scaled data. Principal component analysis (PCA) was constructed based on the scaled data with top 2000 high-variable genes and top 10 principles were used for tSNE construction and UMAP construction. Utilizing graph-based cluster method, the unsupervised cell cluster results based on the PCA top 10 principles were acquired, and the marker genes by FindAllMarkers function with Wilcoxon rank-sum test algorithm were calculated using the following criteria: lnFC > 0.25, p-value<0.05, and min.pct > 0.1. To identify the cell type detailed, the clusters of same cell type were selected for re-tSNE analysis, graph-based clustering, and marker analysis.

Identification of differential gene expression and gene enrichment analysis

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To identify differentially expressed genes among samples, the function FindMarkers with Wilcoxon rank-sum test algorithm was used using the following criteria: lnFC > 0.25, p-value<0.05, and min.pct > 0.1. GO analysis was performed to facilitate elucidating the biological implications of marker genes and differentially expressed genes. The GO annotations from NCBI (http://www.ncbi.nlm.nih.gov/), UniProt (http://www.uniprot.org/), and the Gene Ontology (http://www.geneontology.org/) were downloaded. Fisher’s exact test was applied to identify the significant GO categories, and FDR was used to correct the p-values. Pathway analysis was used to find out the significant pathway of the marker genes and differentially expressed genes according to KEGG database. Fisher’s exact test was applied to select the significant pathway, and the threshold of significance was defined by p-value and FDR. To characterize the relative activation of a given gene set such as pathway activation, QuSAGE (2.16.1) analysis was performed, and related gene sets involving neutrophil function and SASP were according to the publications (Xie et al., 2020; Zhang et al., 2021) and are listed in Supplementary file 2. Briefly, based on the gene set, the gene set variation analysis (GSVA) software package (Hänzelmann et al., 2013) was used to calculate the score of SASP in each cells. Ggpubr R package via the Wilcoxon test (version 0.2.4; https://github.com/kassambara/ggpubr; Kassambara, 2020) was used to analyze changes in the scores between WT and DTAhet mice.

Developmental trajectory inference and RNA velocity analysis

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The single-cell trajectories analysis was applied utilizing Monocle2 (https://cole-trapnell-lab.github.io/monocle-release; Trapnell, 2019) using DDR-Tree and default parameter. Before Monocle analysis, marker genes of the Seurat clustering result and raw expression counts of the cell passed filtering were selected. Based on the pseudotime analysis, branch expression analysis modeling (BEAM Analysis) was applied for branch fate-determined gene analysis. To estimate the cell dynamics, RNA velocity analysis was performed through scVelo package (version 0.2.3) based on ScanPy package (version v1.5.0) with default parameters.

Statistical analysis

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All data were analyzed using GraphPad Prism (v8.2.1) software for statistical significance. p-Value was determined by the Student’s t-test for two-group or one-way ANOVA test for multiple-group comparisons. Gehan–Breslow–Wilcoxon test was used for analyzing Kaplan–Meier curve of WT and DTAhet mice.

Data availability

ScRNA-Seq and RNA-seq data have been deposited into GEO repository with accession codes GSE202516 and GSE202356 respectively. Source data have been deposited in Dryad.

The following data sets were generated
    1. Ding P
    2. Gao C
    3. Gao Y
    4. Gao J
    (2022) NCBI Gene Expression Omnibus
    ID GSE202516. Osteocytes regulate skeletal senescence during development.
    1. Ding P
    2. Gao C
    3. Gao Y
    4. Gao J
    (2022) NCBI Gene Expression Omnibus
    ID GSE202356. Osteocytes regulate skeletal senescence during development.
    1. Ding P
    2. Gao C
    3. Gao Y
    4. Liu D
    5. Li H
    6. Xu J
    7. Chen X
    8. Huang Y
    9. Zhang C
    10. Zheng M
    11. Gao J
    (2022) Dryad Digital Repository
    Data for: Osteocytes regulate senescence of bone and bone marrow.
    https://doi.org/10.5061/dryad.5tb2rbp6k

References

Decision letter

  1. Mei Wan
    Reviewing Editor; Johns Hopkins University, United States
  2. Carlos Isales
    Senior Editor; Augusta University, United States
  3. Jean X Jiang
    Reviewer; The University of Texas Health Science Center at San Antonio, United States
  4. Mei Wan
    Reviewer; Johns Hopkins University, United States

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Osteocytes regulate organismal senescence of bone and bone marrow" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Mei Wan as Reviewing Editor and Reviewer #3, and the evaluation has been overseen by Carlos Isales as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Jean X Jiang (Reviewer #1);

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

There are several concerns raised by the reviewers that need to be addressed. Particularly, it is necessary for the authors to:

1) Validate the targeting specificity of DMP1-Cre (i.e. preclude muscle targeting) and define how bone partial ablation could be achieved in the mouse model used in the study;

2) Confirm the senescence phenotype in bone/bone marrow and provide an explanation on how SASP score is determined from the scRNA-seq data;

3) Clarify how osteoblast differentiation capacity and osteoclastogenesis were affected in vitro in the absence of osteocytes; and

4) Explore/explain the mechanisms by which osteocyte reduction causes cellular senescence of the surrounding cells. In addition, the authors should consider addressing other concerns raised by the reviewers.

Reviewer #1 (Recommendations for the authors):

Quantification and comparison of numbers of skeletal muscle cells between control and DMP-1 DTA will provide a support to exclude the possible activity of DMP1-cre in muscle cells.

Reviewer #2 (Recommendations for the authors):

There is a need to correct the reference to the supplemental figures as the numbering started with Figure 2—figure supplemental 1, and this mismatch occurred throughout the manuscript for the remaining figures.

In the generation of mice with reduced osteocyte numbers through conditional expression of DTA in mice in DMP1 expressing osteocytes, it was unclear how partial ablation could be achieved in DTAf/+ mice, as opposed to DTAf/f mice which is lethal at birth. In DTAf/+ mice, toxins are still being produced, although at lower levels throughout the live time of the osteocytes. Does this imply a random process depending on a threshold that could kill the cell? This needs to be better defined and perhaps a need to provide data on the average number of surviving osteocytes with ageing, as an incremental number of osteocytes dying with age could be a reason for the shortened life span.

In the first part of the result section, chronologically, it may be more logical to describe prenatal data first, followed by the 4-week findings.

Line 151. The conclusion that in the context of the mutant mice, the observed osteoporosis, kyphosis, and sarcopenia represent premature ageing needs better justification and to clearly define premature ageing.

While the in vivo data showed changes in osteogenesis and adipogenesis, the in vitro data for impaired osteoblast function is puzzling as these cells are no longer under the influence of osteocytes in this artificial environment. Are the authors proposing a more permanent effect on the osteocytes isolated? If so, what could be the effectors? The same goes for the in vitro differentiation of the bone marrow derived macrophages (Bmms); are there more macrophages primed for the osteoclast lineage? Again, the study was performed in the absence of osteocytes. Clarification and better explanations are needed.

While the relationship between osteoblasts and osteoclasts is tested from an assessment of RANKL/OPG ratio, the relationship between WNT signaling in osteoblasts and osteoprogenitors and the role of SOST produced by osteocytes need to be addressed to provide a fuller picture on the osteogenic lineage.

The scRNA-seq of the 4-week-old bone marrow cells is key to the hypothesis on the changes in haematopoiesis. The data presented on the RNA velocity was not so clear, and importantly, the hypothesis and focus on senescence was not well explained or justified. There is a feel of "cherry picking", and the method of determining a SASP score from the scRNA-seq data was not described, and how was this derived from the data?

Lines 275-276. The conclusion that "osteocyte reduction induced senescence in osteoprogenitors and myeloid lineage cells" could be an over claim as this is at best, an association as there were no supporting functional data. Similarly, the claim for "organismal senescence" is also too strong a statement, as only bone and bone marrow cells were studied.

Reviewer #3 (Recommendations for the authors):

1. Introduction- The authors should give a comprehensive summary on the current knowledge about the role of osteocytes in normal bone remodeling as well as in age-associated bone loss.

2. Can the authors comment on whether other organ systems (eg. cardiovascular system, brain…) besides musculoskeletal system have defects in the osteocytes ablation model?

https://doi.org/10.7554/eLife.81480.sa1

Author response

Essential revisions:

There are several concerns raised by the reviewers that need to be addressed. Particularly, it is necessary for the authors to:

1) Validate the targeting specificity of DMP1-Cre (i.e. preclude muscle targeting) and define how bone partial ablation could be achieved in the mouse model used in the study;

If Dmp1cre directly targets muscle, we would see a reduction of muscle fibers similar to osteocyte ablation in bone. We thus compare number of muscle fibers between WT and DTAhet mice at 4 weeks and 13 weeks. As shown, the number of muscle fibers were not altered in these Dmp1cre mice at both 4 and 13 weeks (Lines 165-169, Figure 3 —figure supplement 1A and B). In addition, we extracted RNA of muscle and bone from WT mice and performed qPCR to verify the expression of Dmp1. qPCR results revealed minimum expression of Dmp1 in muscle whose cycle threshold was about 29, compared with the expression of Dmp1 in bone matrix (Lines 169-173, Figure 3 —figure supplement 1C). Together, these data demonstrated that sarcopenia-like phenotype was unlikely caused by Dmp1cre expression in muscle cells.

To define how osteocyte partial ablation was achieved, we performed the quantification of empty lacunae ratio of DTAhet mice at 13 weeks. About 80% empty lacunae was observed in DTAhet mice at 13 weeks which increased about 20% compared to 4 weeks (Line 126-130, Figure 1 —figure supplement 1B), indicating diphtheria toxin (DT) has an accumulative effect with age in DTAhet mice. We speculated that when DT accumulated to a threshold, osteocytes were ablated.

2) Confirm the senescence phenotype in bone/bone marrow and provide an explanation on how SASP score is determined from the scRNA-seq data;

SASP score analysis was widely performed to indicate the cellular senescence phenotype (Zhang et al. 2021; Yang et al. 2022; Aging Atlas 2021; Ma et al. 2021; Ma et al. 2020), based on our sc-RNA-seq data, we performed SASP gene set score analysis according to the methods from recent publications (Zhang, et al. 2021) and the gene set has been provided in Supplementary File 2. Briefly, based on the gene set, the gene set variation analysis (GSVA) software package (Hanzelmann, Castelo, and Guinney 2013) was used to calculate the score of SASP in each cells. Ggpubr R package via the Wilcoxin test (https://github.com/kassambara/ggpubr) (version 0.2.4) was used to analyze changes in the scores between WT and DTAhet mice. We have updated the detail about SASP score methods in the Materials and methods accordantly (Lines 717725).

To further confirm the senescence phenotype in bone/bone marrow, we performed the senescence β-galactosidase (SA-βGal) staining of frozen sections of WT and DTAhet mice femur (Figure 6 —figure supplement 1D). There were obvious SA-βGal+ cells in the primary spongiosa, bone marrow and cortical bone in DTAhet mice compared to WT mice. Besides, RNA of bone and bone marrow cells from DTAhet and WT mice was extracted respectively, followed by qPCR verification. There was significantly increased expression of senescence associated markers including Cdkn2a, Cdkn1a in DTAhet mice bone and bone marrow (Figure 6 —figure supplement 1B and C) (Lines 308-316) in DTA mice. Combined with our scRNA-seq data, we are confident to conclude that bone and bone marrow underwent cellular senescence after osteocyte ablation.

3) Clarify how osteoblast differentiation capacity and osteoclastogenesis were affected in vitro in the absence of osteocytes; and

We thank the reviewer for pointing out this issue. We demonstrated that osteoblast and osteoclast activities were affected in vivo after osteocyte ablation, and intriguingly, we found that in vitro differentiation of osteoblast and osteoclast was also affected even in the absence of osteocytes. Previous studies have demonstrated that altered environment can have a long-lasting effects on cells, when these cells are isolated in vitro or even transferred to another mice, they can still perform similar functions or have similar effects based on their memory (Cao et al. 2020; Isaac et al. 2014; Ding et al. 2022; Edgar et al. 2021; Li et al. 2022). Thus, we hypothesized that osteoprogenitors and BMMs have been primed by the altered bone microenvironment in DTAhet mice before in vitro differentiation. In our study, bone microenvironment was changed rigorously after osteocyte ablation. After performing the in vitro differentiation of osteogenesis and osteoclastogenesis from different age of mice (4 weeks and 13 weeks) (Figure 4H-I and Figure 4O-Q), we found impaired osteogenesis and promoted osteoclastogenesis, indicating altered bone microenvironment had significant effects on these cells. Importantly, there was a time-dependent accumulative effect on these cells, as when osteoprogenitors and BMMs were collected from older mice, the in vitro osteogenesis and osteoclastogenesis were much more impaired than the cells from younger mice. The underlying mechanism is worth to be further studied. We now have replenished this in Results (Lines 205-210 and Lines 221-229) and Discussion (Lines 391-400).

4) Explore/explain the mechanisms by which osteocyte reduction causes cellular senescence of the surrounding cells. In addition, the authors should consider addressing other concerns raised by the reviewers.

We thank the reviewer’s suggestion, and we now have updated this in the Discussion in the revised version (Line 420-429). Different cell lineages harbor in the bone marrow and form specific niches. Each niche has their own specific microenvironment which supports stemness, survival and fates. Also, there are complex cell-cell crosstalk between different niches (Hu et al. 2016). We speculated that osteocytes may regulate cell lineage commitment via releasing factors. As the signaling center, osteocytes have been shown to regulate hematopoiesis, bone remodeling and even remote organs functions (Divieti Pajevic and Krause 2019; Asada, Sato, and Katayama 2015). Our study has provided new finding that osteocyte ablation influences cell lineage commitments and induces senescence. Further study is required to determine factors produced by osteocytes that have impact on lineage commitment and cell senescence.

Reviewer #1 (Recommendations for the authors):

Quantification and comparison of numbers of skeletal muscle cells between control and DMP-1 DTA will provide a support to exclude the possible activity of DMP1-cre in muscle cells.

We thank the reviewer’s recommendation, and we quantified and compared the number of skeletal muscle numbers between WT and DTAhet mice. Accordingly, the details were given in Response to Essential Revision 1.

Reviewer #2 (Recommendations for the authors):

There is a need to correct the reference to the supplemental figures as the numbering started with Figure 2—figure supplemental 1, and this mismatch occurred throughout the manuscript for the remaining figures.

We thank the reviewer for pointing out the issue. In our revised version, we have corrected the numbering of supplement figures which starts from Figure 1 —figure supplement 1, and now the remaining figures were matched with the revised manuscript.

In the generation of mice with reduced osteocyte numbers through conditional expression of DTA in mice in DMP1 expressing osteocytes, it was unclear how partial ablation could be achieved in DTAf/+ mice, as opposed to DTAf/f mice which is lethal at birth. In DTAf/+ mice, toxins are still being produced, although at lower levels throughout the live time of the osteocytes. Does this imply a random process depending on a threshold that could kill the cell? This needs to be better defined and perhaps a need to provide data on the average number of surviving osteocytes with ageing, as an incremental number of osteocytes dying with age could be a reason for the shortened life span.

Please refer to Response to Essential Revision 1.

In the first part of the result section, chronologically, it may be more logical to describe prenatal data first, followed by the 4-week findings.

We thank the useful suggestion from the reviewer. In the revised version, we have changed the order and described prenatal data firstly (Lines 119-122), followed by the 4-week findings.

Line 151. The conclusion that in the context of the mutant mice, the observed osteoporosis, kyphosis, and sarcopenia represent premature ageing needs better justification and to clearly define premature ageing.

We thank the reviewer for pointing out the issue. Premature aging we used in Line 151 means accelerated skeletal aging which occurred at the early stage of the life as opposed to normal aging process which occurred at the late stage of the life (Coppede 2012). As DTAhet mice had an obviously shortened lifespan, combined with these age-related phenotypes including osteoporosis, kyphosis and sarcopenia, we concluded that DTAhet mice underwent the premature skeletal aging process. We now have changed the ‘premature aging’ into ‘accelerated skeletal aging’ in Lines 177-179 and clearly defined it.

While the in vivo data showed changes in osteogenesis and adipogenesis, the in vitro data for impaired osteoblast function is puzzling as these cells are no longer under the influence of osteocytes in this artificial environment. Are the authors proposing a more permanent effect on the osteocytes isolated? If so, what could be the effectors? The same goes for the in vitro differentiation of the bone marrow derived macrophages (Bmms); are there more macrophages primed for the osteoclast lineage? Again, the study was performed in the absence of osteocytes. Clarification and better explanations are needed.

We thank the reviewer for pointing out this issue. We have further performed the in vitro osteogenesis and osteoclastogensis obtained from mice of different ages. And we performed flow cytometry analysis and found that osteoclast progenitors were slightly increased (Lines 219-221) (Figure 4 —figure supplement 1G and H). In addition, our sc-RNA seq data demonstrated that osteoclast differentiation related pathway was upregulated in subcluster of monocyte/macrophages (Figure 6 —figure supplement 1H and I). Together, these data indicated that there were more macrophages primed for osteoclast lineage. Accordingly, the details were given in Response to Essential Revision 3.

While the relationship between osteoblasts and osteoclasts is tested from an assessment of RANKL/OPG ratio, the relationship between WNT signaling in osteoblasts and osteoprogenitors and the role of SOST produced by osteocytes need to be addressed to provide a fuller picture on the osteogenic lineage.

According to the reviewer’s suggestion, we further performed GSEA analysis which revealed that osteogenesis related pathways including Wnt signaling pathway, Hedgehog signaling pathway and Notch signaling pathway were downregulated (Figure 4 —figure supplement 1D, E and F) (Lines 193-196). While SOST produced by osteocytes was decreased (Figure 4 —figure supplement 1B and C), we speculated that induction of SASP in osteoprogenitors may be account for the underlying cause. Senescent osteoprogenitors have reduced self-renewal capacity and predominantly differentiate into adipocytes as opposed to osteoblasts (Chen et al. 2016; Li et al. 2017; Rosen et al. 2009). Consistently, our model indicated an increased adipogenesis after osteocyte ablation, and fat-induction factors inhibit osteogenesis during adipogenesis (Chen, et al. 2016). Thus, osteocyte ablation induced senescence accumulation in osteoprogenitors leading to the cell commitment towards adipogenesis with impaired osteogenesis (Lines 369-391).

The scRNA-seq of the 4-week-old bone marrow cells is key to the hypothesis on the changes in haematopoiesis. The data presented on the RNA velocity was not so clear, and importantly, the hypothesis and focus on senescence was not well explained or justified. There is a feel of "cherry picking", and the method of determining a SASP score from the scRNA-seq data was not described, and how was this derived from the data?

Examination of lifespan of DTA mice make us to consider that 4 weeks is the critical time when ablation of osteocyte caused the changes of bone and bone marrow. We thus inferred that bone marrow also underwent significant changes and we further performed scRNA-seq at 4-week-old. RNA velocity is a powerful tool to demonstrate cellular lineage differentiation (Karaman et al. 2022; Bergen et al. 2020; Qiao and Huang 2021). In our study, we found that there was stronger directionality of the velocity vectors in the neutrophil cluster (circled in red) of DTAhet mice compared to WT mice (Figure 5F), thus indicating that HSPC differentiation was altered after osteocyte ablation.

As DTAhet mice had accelerated skeletal aging phenotypes including myelopoiesis, osteoporosis, kyphosis and sarcopenia with shortened lifespan, and we found that these phenotypes like osteoporosis, sarcopenia, kyphosis and sarcopenia became severe with age. Cellular senescence was the obvious hypothesis that caused acceleration of skeletal aging. As SASP score analysis of scRNA-seq has confirmed our hypothesis, we thus ‘cherry picked’ senescence as our focus and underlying mechanism. We now have replenished it in Lines 298-302.

The details about SASP methods were given in Response to Essential Revision 2.

Lines 275-276. The conclusion that "osteocyte reduction induced senescence in osteoprogenitors and myeloid lineage cells" could be an over claim as this is at best, an association as there were no supporting functional data. Similarly, the claim for "organismal senescence" is also too strong a statement, as only bone and bone marrow cells were studied.

We thank the reviewer for pointing out this issue. In the revised version, we adjusted the conclusion we reached which was ‘senescence in osteoprogenitors and myeloid lineage cells was associated with osteocyte reduction’ (Lines 318-320). And we changed the claim for ‘organismal senescence’ into ‘senescence’ instead (Line 1, Lines 39-40, Lines 105-106, Lines 294-295, Lines 436-437).

Reviewer #3 (Recommendations for the authors):

1. Introduction- The authors should give a comprehensive summary on the current knowledge about the role of osteocytes in normal bone remodeling as well as in age-associated bone loss.

We thank the reviewer’s useful recommendation, and we now have updated this in the Introduction in the revised version (Lines 76-92).

2. Can the authors comment on whether other organ systems (eg. cardiovascular system, brain…) besides musculoskeletal system have defects in the osteocytes ablation model?

As far as we know, there were some defects in other organ systems like liver, thymus, peripheral white adipose tissue and even brains which have been reported in Dmp1creDTRfl/fl mice (Sato et al. 2013). They found that osteocyte ablation induced lymphoid organ atrophy, thymocyte depletion and altered fat metabolism. Specifically, their data suggested that osteocytes may control fat maintenance in the whole body, including the circulation, and hypothalamus may cooperate it. These data further confirmed the role of osteocyte as an important regulator to other organ systems. We now have also added it in the Discussion (Lines 429-434).

Reference:

Aging Atlas, Consortium. 2021. Aging Atlas: A Multi-Omics Database for Aging Biology. Nucleic Acids Res 49: D825-D830. https://dx.doi.org/10.1093/nar/gkaa894, PMID: 33119753.

Asada, N., M. Sato, and Y. Katayama. 2015. Communication of Bone Cells with Hematopoiesis, Immunity and Energy Metabolism. Bonekey Rep 4: 748. https://dx.doi.org/10.1038/bonekey.2015.117, PMID: 26512322.

Bergen, V., M. Lange, S. Peidli, F. A. Wolf, and F. J. Theis. 2020. Generalizing Rna Velocity to Transient Cell States through Dynamical Modeling. Nat Biotechnol 38: 1408-1414. https://dx.doi.org/10.1038/s41587-020-0591-3, PMID: 32747759.

Cao, H., Q. Yan, D. Wang, Y. Lai, B. Zhou, Q. Zhang, W. Jin, S. Lin, Y. Lei, L. Ma, Y. Guo, Y. Wang, Y. Wang, X. Bai, C. Liu, J. Q. Feng, C. Wu, D. Chen, X. Cao, and G. Xiao. 2020. Focal Adhesion Protein Kindlin-2 Regulates Bone Homeostasis in Mice. Bone Res 8: 2. https://dx.doi.org/10.1038/s41413-0190073-8, PMID: 31934494.

Chen, Q., P. Shou, C. Zheng, M. Jiang, G. Cao, Q. Yang, J. Cao, N. Xie, T. Velletri, X. Zhang, C. Xu, L. Zhang, H. Yang, J. Hou, Y. Wang, and Y. Shi. 2016. Fate Decision of Mesenchymal Stem Cells: Adipocytes or Osteoblasts? Cell Death Differ 23: 1128-39. https://dx.doi.org/10.1038/cdd.2015.168, PMID: 26868907.

Coppede, F. 2012. Premature Aging Syndrome. Adv Exp Med Biol 724: 317-31. https://dx.doi.org/10.1007/978-1-4614-0653-2_24, PMID: 22411253.

Ding, P., Q. Tan, Z. Wei, Q. Chen, C. Wang, L. Qi, L. Wen, C. Zhang, and C. Yao.

2022. Toll-Like Receptor 9 Deficiency Induces Osteoclastic Bone Loss Via Gut Microbiota-Associated Systemic Chronic Inflammation. Bone Res 10: 42.

https://dx.doi.org/10.1038/s41413-022-00210-3, PMID: 35624094.

Divieti Pajevic, P., and D. S. Krause. 2019. Osteocyte Regulation of Bone and Blood. Bone 119: 13-18. https://dx.doi.org/10.1016/j.bone.2018.02.012, PMID: 29458123.

Edgar, L., N. Akbar, A. T. Braithwaite, T. Krausgruber, H. Gallart-Ayala, J. Bailey, A.

L. Corbin, T. E. Khoyratty, J. T. Chai, M. Alkhalil, A. F. Rendeiro, K. Ziberna,

R. Arya, T. J. Cahill, C. Bock, J. Laurencikiene, M. J. Crabtree, M. E.

Lemieux, N. P. Riksen, M. G. Netea, C. E. Wheelock, K. M. Channon, M.

Ryden, I. A. Udalova, R. Carnicer, and R. P. Choudhury. 2021. Hyperglycemia

Induces Trained Immunity in Macrophages and Their Precursors and Promotes Atherosclerosis. Circulation 144: 961-982.

https://dx.doi.org/10.1161/CIRCULATIONAHA.120.046464, PMID: 34255973.

Hanzelmann, S., R. Castelo, and J. Guinney. 2013. Gsva: Gene Set Variation Analysis for Microarray and Rna-Seq Data. BMC Bioinformatics 14: 7.

https://dx.doi.org/10.1186/1471-2105-14-7, PMID: 23323831.

Hu, X., M. Garcia, L. Weng, X. Jung, J. L. Murakami, B. Kumar, C. D. Warden, I. Todorov, and C. C. Chen. 2016. Identification of a Common Mesenchymal Stromal Progenitor for the Adult Haematopoietic Niche. Nat Commun 7:

13095. https://dx.doi.org/10.1038/ncomms13095, PMID: 27721421.

Isaac, J., J. Erthal, J. Gordon, O. Duverger, H. W. Sun, A. C. Lichtler, G. S. Stein, J. B. Lian, and M. I. Morasso. 2014. Dlx3 Regulates Bone Mass by Targeting

Genes Supporting Osteoblast Differentiation and Mineral Homeostasis in vivo. Cell Death Differ 21: 1365-76. https://dx.doi.org/10.1038/cdd.2014.82, PMID: 24948010.

Karaman, S., S. Paavonsalo, K. Heinolainen, M. H. Lackman, A. Ranta, K. A. Hemanthakumar, Y. Kubota, and K. Alitalo. 2022. Interplay of Vascular Endothelial Growth Factor Receptors in Organ-Specific Vessel Maintenance. J Exp Med 219. https://dx.doi.org/10.1084/jem.20210565, PMID: 35050301.

Li, H., P. Liu, S. Xu, Y. Li, J. D. Dekker, B. Li, Y. Fan, Z. Zhang, Y. Hong, G. Yang, T. Tang, Y. Ren, H. O. Tucker, Z. Yao, and X. Guo. 2017. Foxp1 Controls Mesenchymal Stem Cell Commitment and Senescence During Skeletal Aging. J Clin Invest 127: 1241-1253. https://dx.doi.org/10.1172/JCI89511, PMID: 28240601.

Li, X., H. Wang, X. Yu, G. Saha, L. Kalafati, C. Ioannidis, I. Mitroulis, M. G. Netea, T. Chavakis, and G. Hajishengallis. 2022. Maladaptive Innate Immune Training of Myelopoiesis Links Inflammatory Comorbidities. Cell 185: 17091727 e18. https://dx.doi.org/10.1016/j.cell.2022.03.043, PMID: 35483374.

Ma, S., S. Sun, L. Geng, M. Song, W. Wang, Y. Ye, Q. Ji, Z. Zou, S. Wang, X. He, W. Li, C. R. Esteban, X. Long, G. Guo, P. Chan, Q. Zhou, J. C. I. Belmonte, W.

Zhang, J. Qu, and G. H. Liu. 2020. Caloric Restriction Reprograms the SingleCell Transcriptional Landscape of Rattus Norvegicus Aging. Cell 180: 9841001 e22. https://dx.doi.org/10.1016/j.cell.2020.02.008, PMID: 32109414. Ma, S., S. Sun, J. Li, Y. Fan, J. Qu, L. Sun, S. Wang, Y. Zhang, S. Yang, Z. Liu, Z. Wu, S. Zhang, Q. Wang, A. Zheng, S. Duo, Y. Yu, J. C. I. Belmonte, P. Chan, Q. Zhou, M. Song, W. Zhang, and G. H. Liu. 2021. Single-Cell Transcriptomic Atlas of Primate Cardiopulmonary Aging. Cell Res 31: 415-432.

https://dx.doi.org/10.1038/s41422-020-00412-6, PMID: 32913304.

Qiao, C., and Y. Huang. 2021. Representation Learning of Rna Velocity Reveals Robust Cell Transitions. Proc Natl Acad Sci U S A 118.

https://dx.doi.org/10.1073/pnas.2105859118, PMID: 34873054.

Rosen, C. J., C. Ackert-Bicknell, J. P. Rodriguez, and A. M. Pino. 2009. Marrow Fat and the Bone Microenvironment: Developmental, Functional, and

Pathological Implications. Crit Rev Eukaryot Gene Expr 19: 109-24. https://dx.doi.org/10.1615/critreveukargeneexpr.v19.i2.20, PMID: 19392647. Sato, M., N. Asada, Y. Kawano, K. Wakahashi, K. Minagawa, H. Kawano, A. Sada, K. Ikeda, T. Matsui, and Y. Katayama. 2013. Osteocytes Regulate Primary Lymphoid Organs and Fat Metabolism. Cell Metab 18: 749-58.

https://dx.doi.org/10.1016/j.cmet.2013.09.014, PMID: 24140021.

Yang, C., Y. Pang, Y. Huang, F. Ye, X. Chen, Y. Gao, C. Zhang, L. Yao, and J. Gao. 2022. Single-Cell Transcriptomics Identifies Premature Aging Features of Terc-Deficient Mouse Brain and Bone Marrow. Geroscience.

https://dx.doi.org/10.1007/s11357-022-00578-4, PMID: 35545739.

Zhang, H., J. Li, J. Ren, S. Sun, S. Ma, W. Zhang, Y. Yu, Y. Cai, K. Yan, W. Li, B. Hu, P. Chan, G. G. Zhao, J. C. I. Belmonte, Q. Zhou, J. Qu, S. Wang, and G. H.

Liu. 2021. Single-Nucleus Transcriptomic Landscape of Primate Hippocampal Aging. Protein Cell 12: 695-716. https://dx.doi.org/10.1007/s13238-02100852-9, PMID: 34052996.

https://doi.org/10.7554/eLife.81480.sa2

Article and author information

Author details

  1. Peng Ding

    Department of Orthopaedics, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
    Contribution
    Data curation, Software, Formal analysis, Validation, Visualization, Methodology, Writing - original draft
    Contributed equally with
    Chuan Gao and Youshui Gao
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9348-8134
  2. Chuan Gao

    Department of Orthopaedics, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
    Contribution
    Resources, Data curation, Software, Formal analysis, Visualization, Methodology
    Contributed equally with
    Peng Ding and Youshui Gao
    Competing interests
    No competing interests declared
  3. Youshui Gao

    Department of Orthopaedics, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
    Contribution
    Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology
    Contributed equally with
    Peng Ding and Chuan Gao
    Competing interests
    No competing interests declared
  4. Delin Liu

    1. Centre for Orthopaedic Translational Research, Medical School, University of Western Australia, Nedlands, Australia
    2. Perron Institute for Neurological and Translational Science, Nedlands, Australia
    Contribution
    Resources, Data curation
    Competing interests
    No competing interests declared
  5. Hao Li

    Department of Orthopaedics, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
    Contribution
    Resources, Data curation, Formal analysis
    Competing interests
    No competing interests declared
  6. Jun Xu

    Department of Orthopaedics, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
    Contribution
    Formal analysis, Validation, Methodology
    Competing interests
    No competing interests declared
  7. Xiaoyi Chen

    Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China
    Contribution
    Formal analysis, Validation, Visualization
    Competing interests
    No competing interests declared
  8. Yigang Huang

    Department of Orthopaedics, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
    Contribution
    Resources, Formal analysis, Validation
    Competing interests
    No competing interests declared
  9. Changqing Zhang

    Department of Orthopaedics, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Project administration, Writing – review and editing
    For correspondence
    zhangcq@sjtu.edu.cn
    Competing interests
    No competing interests declared
  10. Minghao Zheng

    1. Centre for Orthopaedic Translational Research, Medical School, University of Western Australia, Nedlands, Australia
    2. Perron Institute for Neurological and Translational Science, Nedlands, Australia
    Contribution
    Conceptualization, Resources, Supervision, Investigation, Visualization, Methodology, Project administration, Writing – review and editing
    For correspondence
    minghao.zheng@uwa.edu.au
    Competing interests
    No competing interests declared
  11. Junjie Gao

    1. Department of Orthopaedics, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
    2. Institute of Microsurgery on Extremities, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Validation, Investigation, Visualization, Project administration, Writing – review and editing
    For correspondence
    colingjj@163.com
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4820-8524

Funding

National Natural Science Foundation of China (82002339)

  • Junjie Gao

National Natural Science Foundation of China (81820108020)

  • Changqing Zhang

Shanghai Frontiers Science Center of Degeneration and Regeneration in Skeletal System (BJ1-9000-22-4002)

  • Changqing Zhang

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (82002339 to JJG, 81820108020 to CQZ) and the Shanghai Frontiers Science Center of Degeneration and Regeneration in Skeletal System (BJ1-9000-22-4002).

Ethics

All mice experiments were approved by the Animal Care and Use Committee of Shanghai Sixth People's Hospital (Permit number: 2021-0935, 2021-0936). All surgery was performed under anesthesia using isoflurane or sodium pentobarbital, and every effort was made to minimize suffering.

Senior Editor

  1. Carlos Isales, Augusta University, United States

Reviewing Editor

  1. Mei Wan, Johns Hopkins University, United States

Reviewers

  1. Jean X Jiang, The University of Texas Health Science Center at San Antonio, United States
  2. Mei Wan, Johns Hopkins University, United States

Publication history

  1. Received: June 29, 2022
  2. Preprint posted: July 18, 2022 (view preprint)
  3. Accepted: October 27, 2022
  4. Accepted Manuscript published: October 28, 2022 (version 1)
  5. Version of Record published: November 21, 2022 (version 2)

Copyright

© 2022, Ding, Gao, Gao et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Peng Ding
  2. Chuan Gao
  3. Youshui Gao
  4. Delin Liu
  5. Hao Li
  6. Jun Xu
  7. Xiaoyi Chen
  8. Yigang Huang
  9. Changqing Zhang
  10. Minghao Zheng
  11. Junjie Gao
(2022)
Osteocytes regulate senescence of bone and bone marrow
eLife 11:e81480.
https://doi.org/10.7554/eLife.81480

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