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. 2025 Jun 26;9(2):026127. doi: 10.1063/5.0271050

Epigenetic regulation and mechanobiological adaptation in tenocytes during maturation

Ellen Y Zhang 1,2,1,2, Tyler E Blanch 1,2,1,2, Saeed B Ahmed 1, Xi Jiang 1, Nathaniel A Dyment 1,2,1,2, Su Chin Heo 1,2,3,1,2,3,1,2,3,a)
PMCID: PMC12204717  PMID: 40584816

Abstract

Tendons are essential for musculoskeletal function, facilitating movement by transmitting forces from muscles to bones. However, aging alters the tendon microenvironment, disrupting the delicate interactions between tenocytes and the extracellular matrix (ECM), contributing to tissue degeneration. While prior studies have characterized the mechanical and structural changes in tendons during maturation, the epigenetic regulation of tenocyte function during aging remains poorly understood. Here, we investigate age-dependent mechanobiological and epigenetic changes in murine tenocytes. Our findings demonstrate that mature tenocytes generate higher traction forces and migrate faster. Furthermore, we reveal increased chromatin condensation in mature tenocytes, accompanied by elevated levels of the repressive histone mark H3K27me3 and reduced levels of the activating mark H3K4me3. Chromatin immunoprecipitation sequencing indicates that these histone modifications regulate genes associated with cellular contractility, ECM production, and mechanotransduction, highlighting the critical role of epigenetic mechanisms in governing tenocyte function. These findings suggest that age-related epigenetic changes may contribute to both the maintenance of tissue homeostasis and the suppression of degenerative diseases in tendons, providing new avenues for therapeutic strategies aimed at restoring tenocyte function and enhancing tendon regeneration.

I. INTRODUCTION

Tendons are critical components of the musculoskeletal system, serving as robust connectors that transmit mechanical forces from muscle contractions to bones, enabling movement and stability.1–3 The highly organized, collagen-rich extracellular matrix (ECM) in tendons provides tensile strength, elasticity, and the capacity to withstand mechanical stress. The functionality and resilience of tendons depend on the complex interplay between tenocytes, their resident cells, and the ECM.1–3

Tenocytes are specialized cells that maintain the structural integrity and functionality of the tendon ECM, which is predominantly composed of type I collagen and proteoglycans.4,5 These cells are highly adaptive, continuously responding to biochemical and biomechanical cues from their microenvironment to regulate their phenotype and ensure tissue homeostasis.4,5 Tendons undergo dynamic biochemical and biomechanical remodeling throughout development, maturation, and aging, with further alterations under pathological conditions.6,7 During early growth, these changes drive tissue formation and functional optimization, while maturation establishes regulatory mechanisms that maintain tendon homeostasis. However, aging disrupts this equilibrium; microenvironmental shifts and cell-intrinsic reprogramming impair tenocyte adaptability, ultimately promoting tendon dysfunction and degeneration.6,7 Understanding these lifespan-dependent changes in tenocyte behavior is critical for deciphering the mechanisms underlying tendinopathies and tendon rupture.8,9 However, while considerable progress has been made in characterizing the mechanical and structural changes in tendons during development and maturation, the molecular mechanisms, especially epigenetic regulation, remain poorly understood.

Epigenetics, defined as heritable changes in gene expression that occur without altering the deoxyribonucleic acid (DNA) sequence, has emerged as a key mechanism by which cells interpret and respond to mechanical and biochemical signals from their environment.10 Epigenetic modifications such as histone methylation, chromatin remodeling, and DNA methylation regulate chromatin accessibility and gene expression, influencing various cellular processes, including differentiation, proliferation, and mechanotransduction.10–14 These regulatory mechanisms are tightly linked to tissue development, aging, and pathology. For example, histone modifications like tri-methylation of lysine 27 on histone H3 (H3K27me3), a marker of transcriptional repression,15–18 increase with age in various tissues,19 reflecting reduced transcriptional activity. Conversely, tri-methylation of lysine 4 on histone H3 (H3K4me3), associated with transcriptional activation,20–24 decreases with age, suggesting reduced expression of genes essential for maintaining tissue homeostasis.25 Despite these general observations, how epigenetic modifications influence tenocyte function during tendon development and aging remains largely unknown.

To address these gaps, we investigate mechanobiological and epigenetic shifts in murine tenocytes as they mature from early postnatal stages to adulthood. Using advanced methodologies such as super-resolution imaging, traction force microscopy (TFM), and molecular assays, we examined the effects of tendon maturation on tenocyte behavior, including cellular contractility, migratory capacity, chromatin organization, and gene regulation. In particular, we focused on the role of histone modifications, such as H3K27me3 and H3K4me3, in modulating changes in tenocyte phenotype and mechanobiological status. This study provides new insights into the epigenetic and biomechanical transitions underlying tendon maturation. We define a link between chromatin architecture and cellular behavior, offering a deeper understanding of the epigenetic landscape of tenocytes and its role in tendon development and pathology. These findings could inform the development of innovative therapeutic strategies to maintain or restore tenocyte function and tendon integrity throughout life.

II. RESULTS

A. Tenocyte migration and traction force are age-dependent

Given that tissue development and maturation influence cellular behaviors such as migration, proliferation, and mechanotransduction,26–28 we first investigated how tendon maturation affects the baseline mechanobiological properties of tenocytes by comparing developmental (4–4.5 weeks) and mature (40–45 weeks) mouse tail tendon tenocytes [Fig. 1(a)]. To assess age-related differences in tenocyte migratory capacity, we performed a wound closure assay (WCA) over 12 hours. Our results showed that mature tenocytes exhibited significantly faster migration speeds than developmental tenocytes [Figs. 1(b) and 1(c)]. Since cellular traction forces reflect cytoskeletal remodeling and play a key role in regulating migration,29–31 we next utilized traction force microscopy (TFM) to measure total force and traction stress in the cells. Mature tenocytes exerted significantly higher total force and average traction stress than developmental tenocytes [Figs. 1(d) and 1(e)], suggesting that increased cellular contractility may contribute to their enhanced migratory capacity. These results indicate that tendon cell maturation is accompanied by biomechanical adaptations that promote cell motility, potentially influencing age-related changes in tendon homeostasis.

FIG. 1.

FIG. 1.

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Age-dependent differences in tenocyte migration and traction forces. (a) Schematic illustrating the isolation of developmental (4–4.5 weeks) and mature (40–45 weeks) tenocytes from mouse tail tendons. All experiments were performed using early-passage cells (passages 1–2) to preserve phenotypic integrity. (b) Representative wound closure assay (WCA) images of developmental and mature tenocytes at 0 and 12 h. (c) Quantification of wound closure percentage over time (0, 4, 8, and 12 h) for developmental and mature tenocytes (**p < 0.01). (d) Representative traction stress vector maps of developmental and mature tenocytes cultured on 10 kPa substrates (dotted line: cell outline). (e) Quantification of normalized total force and average traction stress per cell, showing significantly higher traction forces in mature tenocytes (n = 37 for developmental, n = 33 for mature; mean ± SEM; **p < 0.01, ****p < 0.0001).

B. Age-driven transcriptomic reprogramming in tenocytes with enhanced contractility

To investigate the molecular basis of increased cell contractile forces in mature tenocytes, we performed bulk RNA sequencing (RNA-Seq) to compare transcriptomic changes between developmental and mature cells. Principal component analysis (PCA) revealed a clear age-dependent separation [Fig. 2(a)], and differential expression analysis identified distinct transcriptomic profiles between the two groups, with 648 genes upregulated and 1051 genes downregulated in mature tenocytes [Fig. 2(b), supplementary material, Fig. S1(a)]. Gene ontology (GO) enrichment analysis of differentially expressed genes (DEGs) was used to understand the biological significance of these transcriptomic differences. The GO enrichment analysis revealed that biological pathways related to the cytoskeleton, including “actin cytoskeleton” and “actin filament,” were highly elevated in mature cells [Fig. 2(c), supplementary material, Figs. S1(b)–S1(d)]. Although the GO terms related to cytoskeletal organization and extracellular matrix remodeling were selected for their direct relevance to tenocyte function, it is important to note that these biological process (BP) terms, while significantly enriched, were not among the top-ranked based on adjusted p-value. We found that many of the top-ranked BP terms in the full analysis were instead associated with immune activation and inflammatory signaling [supplementary material, Fig. S1(b)]. Although not the focus of our study, these results suggest that inflammatory signaling pathways may be also modulated during tenocyte maturation and warrant further investigation. Notably, key ECM genes abundant in tendon such as TNC, Col1A1, Col1A2, and Col3A1, along with cytoskeletal and cellular contractile regulators, including MYH9, MYL9, ACTA1, and ACTA2, were significantly upregulated in mature cells compared to developmental cells [Fig. 2(d)]. Given the essential roles of these genes in actomyosin contractility and mechanotransduction, their elevated expression likely contributes to the increased traction forces and enhanced migration capacity observed in mature tenocytes, as well as enhanced extracellular matrix (ECM) production. These transcriptomic changes indicate that tendon maturation is accompanied by a functional shift toward a more contractile phenotype.

FIG. 2.

FIG. 2.

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Transcriptomic differences between developmental and mature tenocytes. (a) Principal component analysis (PCA) plot illustrating distinctions between developmental and mature tenocytes based on their global gene expression profiles. (b) Volcano plot showing differentially expressed genes (DEGs) between developmental and mature tenocytes, with significantly upregulated and downregulated genes highlighted. (c) Gene Ontology (GO) enrichment analysis of upregulated genes in mature tenocytes, highlighting significantly enriched biological process (BP), molecular function (MF), and cellular component (CC) terms. These GO terms were selected based on their biological relevance to cytoskeletal regulation and tenocyte-specific function. (d) Bar graphs showing expression levels using transcripts per million (TPM) values of tendon ECM- and contractility-associated genes in developmental and mature tenocytes (n = 4 biological replicates). Significance is a result of the DESeq2 differential analysis, not results from t-tests between the TPM values of development and mature tenocytes. Genes with p-value < 0.05 indicate significant upregulation during maturation.

C. Impact of maturation on chromatin structure and epigenetic regulation in tenocytes

Given the transcriptional differences between developmental and mature tenocytes and the established role of chromatin organization in regulating gene expression,10,32 we hypothesized that tendon maturation alters chromatin condensation and organization. To investigate age-associated nuclear architecture, we employed super-resolution stochastic optical reconstruction microscopy (STORM) to visualize global nanoscale chromatin structure by labeling histone H2B in developmental and mature tenocytes. Our analysis revealed significantly increased chromatin condensation in mature tenocytes compared to developmental tenocytes [Figs. 3(a) and 3(b)], suggesting that maturation induces chromatin-level structural reorganization, potentially contributing to a more transcriptionally repressed state.

FIG. 3.

FIG. 3.

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Nanoscale chromatin organization differences between developmental and mature tenocytes. (a) STORM-based Voronoi polygon density heatmaps of chromatin organization in developmental and mature tenocytes. Scale bars: 1 μm (left), 300 nm (right). (b) Percentage change in chromatin condensation level, showing increased chromatin compaction in mature tenocytes (n = 9) compared to developmental tenocytes (n = 10). Data present mean ± SEM; ****p < 0.0001 (Student's t-test). (c) Representative immunofluorescence images of H3K4me3 (green) in developmental and mature tenocyte nuclei. Scale bar: 20 μm. (d) Representative immunofluorescence images of H3K27me3 (red) in developmental and mature tenocyte nuclei. Scale bar: 20 μm. Quantification of nuclear fluorescence intensity for (c) H3K4me3 and (d) H3K27me3 (n > 144 nuclei from three biological replicates). Data present normalized fluorescence intensity ± SEM; ****p < 0.0001 (Student's t-test).

Given the direct role of histone modifications in chromatin organization,32 we next examined how maturation affects the epigenetic landscape of tenocytes. Specifically, we analyzed the expression levels of key histone marks, H3K4me3 (tri-methylation of lysine 4 on histone H3) and H3K27me3 (tri-methylation of lysine 27 on histone H3), which are associated with transcriptional activation24,33 and repression,33,34 respectively. Immunofluorescence analysis showed a decrease in H3K4me3 levels and a notable increase in H3K27me3 levels in mature tenocytes [Figs. 3(c) and 3(d), supplementary material, Figs. S2(a) and S2(b)]. These findings suggest that maturation shifts the epigenetic landscape toward a more repressive chromatin state, which may drive the diminished transcriptional activity observed in mature tenocytes.

D. H3K4me3 and H3K27me3 mediate epigenetic remodeling during tenocyte maturation

Given that tendon maturation alters chromatin condensation and histone modifications, we investigated the role of these changes in tenocyte maturation-related gene expression using chromatin immunoprecipitation sequencing (ChIP-Seq) in developmental and mature tenocytes.

Heatmaps of H3K4me3 peak binding at the transcription start site (TSS) revealed distinct, sharp peaks concentrated around the TSS, indicating high levels of H3K4me3 modification at promoter regions25,35,36 in both cell types [Figs. 4(a) and 4(b)]. Read distribution analysis showed broader H3K4me3 occupancy in mature tenocytes compared to developmental tenocytes [Fig. 4(a)]. H3K4me3 was strongly enriched at genes associated with cytoskeletal organization and contractility, such as MYH9, MYL9, and ACTA2 [Fig. 4(c)], all of which showed higher expression in mature tenocytes [Fig. 2(d)]. These findings correlate with the observed increase in cell migration speed [Figs. 1(b) and 1(c)] and traction force [Figs. 1(d) and 1(e)] in mature tenocytes, suggesting that H3K4me3 plays a pivotal role in regulating contractility and cytoskeletal remodeling during tenocyte maturation.

FIG. 4.

FIG. 4.

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H3K4me3 regulates phenotypic differences between developmental and mature tenocytes. (a) Heatmaps showing H3K4me3 peak binding at transcription start site (TSS) regions in developmental and mature tenocytes. (b) Bar plots showing the genomic annotation of H3K4me3 peak locations in developmental and mature tenocytes. (c) Integrative Genomics Viewer (IGV) screenshots displaying H3K4me3 peak intensities at selected contractility-related genes in developmental and mature tenocytes. (d) IGV screenshots showing H3K4me3 peak intensities at selected tenogenic and chromatin-related genes in developmental and mature tenocytes (n = 3/group, from three biological replicates). (e) Heatmaps showing H3K27me3 peak binding at TSS regions in developmental and mature tenocytes. (f) Bar plots showing the genomic annotation of H3K27me3 peak locations in developmental and mature tenocytes. (g) IGV screenshots showing H3K27me3 peak intensities at selected top-downregulated genes in developmental and mature tenocytes. (h) IGV screenshots showing H3K27me3 peak intensities at selected tenogenic-related genes in developmental and mature tenocytes (n = 3/group, from three biological replicates).

Additionally, H3K4me3 peaks were enriched at tendon ECM-related genes, including COL1A1, COL3A1, and TNC, in mature tenocytes [Fig. 4(d)]. These data align with RNA-Seq results showing their upregulation in mature tenocytes [Fig. 2(d)], suggesting that histone modifications fine-tune tenogenic gene expression during tendon maturation. Furthermore, the loci encoding Enhancer of zeste homolog 1 and 2 (EZH1 and EZH2), the catalytic subunits of polycomb repressive complex 2 (PRC2), exhibited higher H3K4me3 enrichment in mature tenocytes [Fig. 4(d)]. PRC2 establishes and maintains repressive chromatin,37 and increased H3K4me3 enrichment at these loci suggests epigenetic upregulation of PRC2 components. This may contribute to the increased chromatin condensation observed in mature tenocytes [Figs. 3(a) and 3(b)], indicating a potential regulatory feedback loop where active histone mark deposition at PRC2 loci reinforces chromatin compaction, further stabilizing transcriptional repression in mature cells.

H3K27me3, a well-known repressive histone mark,38–40 is distributed across larger gene regions and is primarily located in intergenic regions in both developmental and mature cells [Figs. 4(e) and 4(f)]. H3K27me3 serves as a repressive epigenetic marker that silences gene transcription.33,34 Indeed, H3K27me3 was enriched at genes significantly downregulated in mature tenocytes, including Ctx3, WNT7A, CPVL, Aspg, and AOX4 [Fig. 4(g)]. Notably, these genes, which are involved in disease association, metabolic regulation, oxidative stress response, and tissue homeostasis,41–45 exhibited strong H3K27me3 enrichment. In contrast, H3K27me3 enrichment was significantly lower at tendon ECM-related genes such as Col1A1 and Col1A2 [Fig. 4(h)], suggesting that H3K27me3-mediated repression primarily silences disease-related pathways while allowing active transcription of tendon ECM genes during tendon maturation. These findings indicate that H3K4me3 and H3K27me3 play crucial roles in epigenetic remodeling during tenocyte maturation, probably ensuring proper tenocyte differentiation while preventing aberrant gene activation that could disrupt tendon homeostasis.

III. DISCUSSION

Tendon injuries and degenerative disorders pose significant clinical challenges, particularly in aging populations where healing capacity is often diminished.6 While the structural and mechanical properties of tendons have been well characterized during growth and maturation,6–8 the molecular and epigenetic mechanisms underlying age-related shifts in tenocyte function remain poorly understood. Tendon homeostasis relies on a delicate balance of extracellular matrix (ECM) turnover, cellular contractility, and mechanotransduction to maintain structural integrity and function under mechanical stress.1,2,4,5 However, tendon degeneration and disease disrupt this balance, leading to increased proteoglycan content, reduced elasticity, and impaired load-bearing capacity, all of which contribute to reduced regenerative potential and increased risk of tendon rupture.6,8,9

In this study, we identified significant age-dependent changes in tenocyte behavior, particularly in cell migration and mechanotransduction. Wound closure assays and traction force microscopy revealed that mature tenocytes exhibited increased traction stress and enhanced migration, suggesting that cytoskeletal adaptations during tendon development may facilitate ECM remodeling. Transcriptomic analyses further showed the upregulation of pathways related to actin binding, cell adhesion, and ECM organization in mature tenocytes, indicating a shift toward a stable yet dynamic cytoskeletal architecture, likely as an adaptive response to increasing tissue stiffness during maturation. Interestingly, beyond these cytoskeletal and matrix-related changes, transcriptomic profiling also revealed significant enrichment of immune-related biological process GO terms in mature tenocytes. This suggests that tenocyte maturation may involve shifts in immunomodulatory signaling. Future studies should examine how these immune pathways interact with mechanical and epigenetic cues to regulate tendon homeostasis and age-related degeneration. Additionally, the wound closure assays reveal a significant age-dependent difference in tenocyte migration. While it does not fully replicate the complexity of the in vivo tendon environment, it allows us to examine fundamental mechanobiological properties such as cell migration and baseline cellular contractility. This study provides valuable insights into baseline maturation-associated mechanobiological changes in tenocytes; however, future studies using more physiologically relevant systems, such as ex vivo tendon explants and in vivo injury systems, will be essential to validate these findings and better understand tenocyte function in the context of tendon healing.

To further investigate the epigenetic mechanisms driving these maturation-associated changes, we employed super-resolution stochastic optical reconstruction microscopy (STORM). Mature tenocytes showed increased nanoscale chromatin condensation, along with elevated levels of the repressive histone modification H3K27me3 and decreased levels of the activating mark H3K4me3. These observations were consistent with quantitative STORM imaging of H2B, which demonstrated increased chromatin condensation and reduced transcriptional activity. Epigenetic modifications in tenocytes during maturation may also be influenced by changes in tendon stiffness, as mechanical cues are known to regulate chromatin architecture and gene expression.46,47 Understanding this mechanobiological-epigenetic interplay is essential for developing therapies aimed at restoring tenocyte adaptability and enhancing tendon healing. Moreover, the impact of epigenetic regulation during maturation and aging extends beyond tendons. In skeletal muscle, aging has been associated with increased histone methylation at loci linked to muscle atrophy, leading to diminished regenerative potential.48,49 Similarly, in cartilage, aging results in an increase in repressive modifications like H3K27me3, impairing chondrocyte function.34 Additionally, age-related epigenetic drift has been observed in hematopoietic stem cells, disrupting gene regulatory networks and impairing cellular function.50 These parallels suggest a conserved mechanism across tissues, where mechanical forces influence chromatin remodeling and impact cellular homeostasis and repair potential. Our chromatin immunoprecipitation sequencing (ChIP-Seq) analysis further supports this model, showing that H3K4me3 enrichment (regulated by the NURF complex51) at promoter sites of genes associated with cellular contractility, tenogenic markers, and growth factors was more pronounced in mature tenocytes than in developmental cells. These findings align with our observation that tenocyte maturation is accompanied by increased cellular contractility, potentially due to a stiffening tissue microenvironment. Additionally, we found enhanced H3K4me3 enrichment, at genes related to ECM structure and stiffness. Conversely, maturation also led to increased H3K27me3 activation (mediated by the PRC2 complex37) repressing genes associated with disease and stress response pathways.

Taken together, our findings suggest that distinct histone modifications, H3K4me3 and H3K27me3, play a pivotal role in regulating cytoskeletal remodeling, ECM regulation, and homeostasis during tenocyte maturation. The enrichment of H3K4me3 at contractility- and tendon ECM-associated genes supports a mechanically active phenotype in mature tenocytes, while H3K27me3-mediated repression of disease- and stress response-related pathways may serve as a protective mechanism against mechanical stress-induced damage. These data indicate that increased H3K4me3 occupancy at genes associated with cytoskeletal contractility and ECM production in mature tenocytes may reinforce the mechanical and structural properties required for tendon function52–54 (Fig. 5). Conversely, the enrichment of H3K27me3 at genes may restrict disease-related biological functions during maturation (Fig. 5). While the observed correlation between cytoskeletal changes and chromatin condensation is intriguing, we acknowledge that a direct causal link between these two processes has not been established with maturation in this study. Thus, future studies, including the measurement of upstream chromatin regulators such as NURF51,77 and EZH1/2 (Refs. 34 and 37) will be necessary to confirm these upstream changes and to understand how these processes may be altered in tendon maturation and disease. In addition, our study identifies key epigenetic alterations during tenocyte maturation that contributes to tissue homeostasis and provides a baseline framework for understanding how histone modification, such as H3K4me3 and H3K27me3, support proper differentiation and mechanobiological function under normal developmental conditions. However, additional future investigations comparing mature and aged tenocytes, particularly under stress or injury conditions will be critical for elucidating how age-related epigenetic drift or failure of chromatin regulation may impair tendon regeneration and contribute to degenerative tendon diseases.

FIG. 5.

FIG. 5.

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Proposed model of histone modifications in tenocyte maturation and homeostasis. Schematic representation illustrating the roles of H3K4me3 and H3K27me3 in tenocyte function. H3K4me3 (mediated by NURF, a nucleosome remodeling enzyme51,77) is linked to cellular contractility, extracellular matrix (ECM) production, and tissue stiffness, supporting tendon maturation. In contrast, H3K27me3, modulated by EZH1/234,37 represses disease-associated pathways, contributing to tenocyte homeostasis.

While our study provides critical mechanistic insights, several limitations must be acknowledged. Our analyses were conducted in vitro, and in vivo validation using animal models or human tendon samples is necessary to confirm clinical relevance. Additionally, while we focused primarily on histone modifications, other epigenetic regulatory mechanisms such as DNA methylation and non-coding RNA-mediated gene regulation may also play significant roles in tenocyte aging and warrant further investigation. Future studies should explore whether modulating specific epigenetic marks can enhance tendon regeneration and whether targeted therapies can restore the proliferative capacity of aged tenocytes. In addition, since this study used only male mice to minimize variability, future studies including female mice will be important to assess sex-specific differences in tenocyte epigenetics and mechanobiology.

We also note that tail tendon tenocytes were used in this study due to their structural uniformity, with highly aligned type I collagen fibers, and accessibility,55 which allow for consistent isolation and sufficient cell yields from individual animals without pooling multiple donors, minimizing biological variability.56 However, tail tendons are non-load bearing, and their mechanical properties, ECM composition, and cellular responses differ from those of load-bearing tendons like the Achilles. Because of these differences, findings from tail tendons may not directly translate to load-bearing tendons like the Achilles,57,58 as prior studies have reported distinct immune and gene expression profiles in tail tendons compared to energy-storing tendons.59,60 Thus, while our findings offer insights into tenocyte maturation, future studies in load-bearing tendon models are essential to establish the broader physiological relevance of these epigenetic and mechanobiological mechanisms.

In addition, we acknowledge that most assays in this study, including RNA-Seq and ChIP-Seq, were conducted on cells cultured on stiff tissue culture plastic (TCP), which may influence cell behavior. However, all age-group comparisons used rigorously early-passage cells (P1-P2) under identical conditions, minimizing substrate effects. Notably, the increased contractility observed in mature tenocytes on physiologically relevant 10 kPa hydrogels aligned with cytoskeletal gene activation determined by RNA-Seq seen on TCP, suggesting these changes may reflect intrinsic maturation. Future studies should validate these findings using three-dimensional (3D) tendon-mimetic matrices, assess epigenetic crosstalk, and examine how in vivo conditions shape chromatin regulation across tendon subtypes.

Finally, understanding the epigenetic regulation of tenocyte function has significant implications for tendon repair and regenerative medicine. Targeting histone modifications or chromatin remodeling factors could present new therapeutic strategies for enhancing tendon healing, particularly in aging populations. Additionally, biomechanical interventions, such as controlled mechanical loading, may be utilized to modulate epigenetic states and improve tendon regeneration. These insights open the door for patient-specific approaches to tendon repair, ultimately leading to better clinical outcomes.

In conclusion, this study provides a comprehensive analysis of age-dependent mechanobiological and epigenetic changes in tenocytes. We demonstrate that tendon maturation drives tenocytes toward a phenotype characterized by increased contractility and reduced proliferative potential. Histone modifications play a critical role in regulating these transitions, modulating gene expression programs essential for maintaining tendon integrity. Understanding these underlying mechanisms offers new therapeutic opportunities to enhance tendon regeneration and counteract age-related degeneration. Our findings contribute to a broader understanding of the mechanobiological and epigenetic landscape of tenocytes in mature tendons and their role in tissue degeneration, with implications for improving tendon healing and clinical outcomes.

IV. METHODS

A. Cell isolation and culture

Primary developmental (4–4.5 weeks) and mature (40–45 weeks) mouse tenocytes were isolated from tail tendons of CD1, pure CD57Bl6, or mixed CD1/CD57Bl6 male mice using an enzymatic digestion protocol.56 Briefly, extracted tendons were pooled and digested in a solution containing 0.4% collagenase IV (Worthington, LS004188) and 0.3% dispase II (ThermoFisher, 17105041) at 37 °C with shaking for 2 h. The resulting cell suspension was filtered, centrifuged, and resuspended in the basal growth medium [Dulbecco's Modified Eagle Medium (DMEM, ThermoFisher, 11965118), supplemented with 10% fetal bovine serum (FBS, R&D Systems, S11150) and 1% penicillin–streptomycin (PS; Corning, 30-002-CI)]. Cells were maintained at 37 °C in a humidified incubator with 5% CO2, with media changes every 2–3 days. All experiments were conducted with early-passage cells (passage 1–2).

B. Wound closure assay (WCA)

The migratory capacity of developmental and mature tenocytes was assessed using a wound closure assay (WCA). Passage 1 (P1) cells were seeded in twelve-well tissue culture plates (2 × 105 cells per well) and cultured for 1–2 days until reaching approximately 90% confluency. A linear scratch was created at the center of each well using a 20 μL pipette tip. Images were acquired at 0, 4, 8, and 12 h post-scratch using an inverted microscope (Nikon ECLIPSE TS100). The wound closure percentage was quantified using ImageJ.

C. Traction force microscopy (TFM)

Traction force microscopy (TFM) was performed to assess cellular contractility following established protocols.61–63 Polyacrylamide (PA) hydrogels with an elastic modulus of 10 kPa (verified via AFM force spectroscopy) were prepared as previously described.61–63 Hydrogels were polymerized under glass coverslips using 0.2% TEMED (BioRad, 1610800) and 10% APS (BioRad, 1610700). After polymerization, gels were activated with 2 mg/mL sulfo-SANPAH (Pierce Protein Biology/Life Technologies, 22589) and coated with fibronectin (20 μg/mL; Sigma Aldrich, F1141) for 1 h at room temperature. Gels were UV-sterilized before cell seeding. developmental and mature tenocytes were seeded at 1000 cells/cm2 and cultured for 18 h in basal media prior to TFM. Phase-contrast images of cells and fluorescence images of embedded 0.2-μm-diameter microspheres (Invitrogen, F8810) were acquired using a Zeiss Axio Observer fluorescence microscope (40× magnification). Image sequences were captured before and after cell lysis (10% SDS, 1% Triton X-100). Bead displacement was analyzed, and traction forces were calculated using Fourier transform traction cytometry (FTTC) implemented in ImageJ and MATLAB.61–63

D. Super-resolution stochastic optical reconstruction microscopy (STORM)

Stochastic optical reconstruction microscopy (STORM) was used to quantify chromatin condensation in developmental and mature tenocytes. Cells were seeded in Nunc™ Lab-Tek™ II 8-well chambered coverglass (ThermoFisher, 155409PK) and cultured for 2 days. Fixation was performed using a methanol–ethanol (1:1) mixture at −20 °C for 6 min, followed by three washes with PBS. Blocking was carried out with BlockAid solution (ThermoFisher, B10710) for 1 h at room temperature to reduce nonspecific binding.

Cells were incubated overnight at 4 °C with rabbit anti-histone H2B antibody (1:50; Proteintech, 15857-1-AP), followed by three PBS washes. Secondary antibodies were labeled with activator-reporter dye pairs (Alexa Fluor 405-Alexa Fluor 647; Invitrogen, A30000 and A20006) and incubated for 1 h at room temperature.

STORM imaging was performed using an ONI Nanoimager. Imaging was conducted in cycles, alternating one activation frame (405 nm) with three imaging frames (647 nm). A fresh oxygen scavenger imaging buffer was prepared according to established protocols,47,62,64,65 containing 10 mM cysteamine MEA (Sigma-Aldrich, 30070-50G), 0.5 mg/mL glucose oxidase (Sigma-Aldrich, G2133), 40 mg/mL catalase (Sigma-Aldrich, C100), and 10% glucose (ThermoFisher, A16828-36) in PBS. Chromatin condensation was quantified using Voronoi tessellation, where smaller polygons indicated higher fluorophore localization density. The nuclear area was calculated by summing all Voronoi polygon areas, excluding large peripheral ones.47,62,64,65 To compare heterochromatin organization between developmental and mature tenocytes, Voronoi polygon areas were normalized by their mean, resulting in reduced Voronoi densities, which were pooled and plotted as cumulative distributions. Chromatin domains were defined by grouping adjacent polygons below a threshold. All analyses were performed in MATLAB.47,62,64,65

E. Immunofluorescence imaging

To assess histone modification status changes with aging, cells were seeded onto 8-well chambered coverglass and cultured for 2 days. Cells were fixed with 4% paraformaldehyde (ThermoFisher, J19943) for 30 min at room temperature, then permeabilized in PBS containing 0.05% Triton X-100 (Sigma-Aldrich, T8787), 320 mM sucrose (Sigma-Aldrich, S0389), and 6 mM magnesium chloride (Sigma-Aldrich, 208337) for 5 min.

For fluorescence labeling, cells were incubated overnight at 4 °C with rabbit anti-H3K27me3 (1:300; ThermoFisher, PA5-31817) and rabbit anti-H3K4me3 (1:300; ThermoFisher, MA5-11199) in PBS. The following day, cells were incubated with Alexa Fluor goat anti-rabbit secondary antibody (1:200; ThermoFisher, A-11010) for 1 h at room temperature.

Images were acquired using a widefield Leica DM6000 microscope (20× magnification). Fluorescence intensity per nucleus was quantified using ImageJ.

F. Bulk RNA sequencing (RNA-Seq) and transcriptome analysis

Bulk RNA sequencing (RNA-Seq) was performed to investigate transcriptomic differences between developmental and mature tenocytes. Passage 1 (P1) cells were seeded in six-well plates at 1 × 106 cells per well and cultured for 1–2 days. Total RNA was extracted using TRIzol Reagent (Invitrogen, 15596026) and purified with the Direct-zol RNA Microprep Kit (Zymo Research, R2062) according to the manufacturer's instructions. RNA integrity and concentration were assessed using a NanoDrop Spectrophotometer (Thermo Fisher Scientific), Qubit Fluorometer (Thermo Fisher Scientific), and Agilent 2100 Bioanalyzer (Agilent Technologies). Only samples with an RNA integrity number (RIN) > 8.0 were used for sequencing.

RNA-Seq libraries were prepared using rRNA depletion and sequenced as 150 bp paired-end reads on an Illumina platform (Genewiz, South Plainfield, NJ). Raw sequence reads were trimmed to remove adapter sequences and low-quality bases using Trimmomatic v0.36. Trimmed reads were aligned to the Mus musculus reference genome (Ensembl) using the STAR aligner66 v2.5.2b with default parameters. Gene-level read counts were quantified using featureCounts67 (Subread package v1.5.2).

Differential expression analysis was conducted using the DESeq2 package in R.68 Differentially expressed genes (DEGs) were identified using a Benjamini–Hochberg adjusted p-value < 0.05 and an absolute log2 fold-change > 1.0. Global transcriptional changes were visualized using EnhancedVolcano,69 while heatmaps illustrating expression patterns of genes of interest were generated using the heatmap package in R. Gene enrichment analysis was performed using clusterProfiler in R, with enriched Gene Ontology (GO) biological processes identified using a false discovery rate (FDR) threshold of ≤0.05. Representative GO terms were visualized using ggplot2.70

G. Chromatin immunoprecipitation sequencing (ChIP-Seq) and data analysis

ChIP-Seq was performed to investigate the roles of histone modification marks in developmental and mature tenocytes. Chromatin was prepared using the SimpleChIP® Enzymatic Chromatin IP Kit (Cell Signaling, #9003) according to the manufacturer's instructions. Briefly, 4 × 106 developmental and mature tenocytes were cross-linked with 1% formaldehyde (Sigma-Aldrich, 252549) for 10 min at room temperature. Nuclei were isolated and digested with micrococcal nuclease (0.5 μL per 4 × 106 cells) for 20 min at 37 °C. Additional sonication (Covaris S220, 15 min, high-intensity setting) was performed to ensure further fragmentation. Chromatin lysates were centrifuged (16 000 g, 10 min, 4 °C), and protein concentrations were measured using a Bradford assay.

For ChIP, 5–10 μg of chromatin was incubated overnight at 4 °C with 2 μg of antibody targeting H3K27me3 (Cell Signaling, C36B11) or H3K4me3 (Cell Signaling, 9751T), conjugated to Protein G magnetic beads (Cell Signaling, #9006). Input controls (5% chromatin) were used for normalization. Cross-links were reversed by overnight incubation at 65 °C, followed by RNase A treatment (0.2 mg/mL, 37 °C, 2 h) and Proteinase K treatment (0.2 mg/mL, 55 °C, 2 h) before DNA purification via spin columns.

Purified ChIP and input DNA were quantified using a Qubit Fluorometer (Thermo Fisher), and fragment size distributions were assessed using a Bioanalyzer (Agilent, High Sensitivity DNA Kit, #5067-4626). Libraries were prepared using the NEBNext Ultra II DNA Library Prep Kit for Illumina (NEB, #E7645) and indexed with SimpleChIP ChIP-Seq Multiplex Oligos for Illumina (Cell Signaling, #29580). qPCR quantification was performed using the NEBNext Library Quant Kit (NEB, #E7630). Libraries were pooled and sequenced on an Illumina NextSeq 500/550 platform (single-end, 75 bp reads).

Raw sequencing reads were quality-checked using FastQC and trimmed for adapters and low-quality bases using Trimmomatic.71 Reads were aligned to the mm10 mouse genome using Bowtie272 with default parameters, and duplicates/low-quality reads were removed using SAMtools.73 Peak calling was conducted using MACS274 (q-value < 0.01), with input controls used for background normalization. Peaks were annotated to the nearest genes using ChIPseeker,75 and signal tracks were normalized to 1M reads for visualization in the IGV genome browser.76 Functional enrichment analysis of ChIP peaks was conducted using clusterProfiler,70 with significantly enriched GO biological processes (FDR < 0.05) visualized via ggplot2.

H. Statistical analysis

The number of biological replicates (n), statistical tests used, and p-values for each experiment are detailed in the figure legends. Statistical analyses were conducted using GraphPad Prism 10 (La Jolla, CA). For comparisons between the two groups, an unpaired Student's t-test was performed. For multiple group comparisons, a two-way analysis of variance (ANOVA) with Tukey's honestly significant difference (HSD) post hoc test was applied. For transcriptomic data analysis, significance was determined using a Benjamini–Hochberg adjusted p-value <0.05 and an absolute log2(fold-change) > 1.0 threshold. A p-value of <0.05 was considered statistically significant.

SUPPLEMENTARY MATERIAL

See the supplementary material for additional figures.

ACKNOWLEDGMENTS

This research was supported by grants from the National Institutes of Health (Nos. K01 AR07787, P50 AR080581, and R01 AR079224) and the NSF Science and Technology Center for Engineering Mechanobiology (No. CMMI-1578571).

Note: This paper is part of the Special Topic on Mechanomedicine.

AUTHOR DECLARATIONS

Conflict of Interest

The authors have no conflicts to disclose.

Ethics Approval

Ethics approval is not required.

Author Contributions

Ellen Y. Zhang: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Writing – original draft (equal); Writing – review & editing (equal). Tyler E. Blanch: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Writing – original draft (equal); Writing – review & editing (equal). Saeed B. Ahmed: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Writing – original draft (equal); Writing – review & editing (equal). Xi Jiang: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Writing – original draft (equal); Writing – review & editing (equal). Nathaniel A. Dyment: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Writing – original draft (equal); Writing – review & editing (equal). Su Chin Heo: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Funding acquisition (equal); Project administration (equal); Supervision (equal); Writing – original draft (equal); Writing – review & editing (equal).

DATA AVAILABILITY

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  • 1.Thorpe C. T. and Screen H. R. C., “Tendon structure and composition,” in Metabolic Influences on Risk for Tendon Disorders, edited by Ackermann P. W. and Hart D. A. (Springer International Publishing, Cham, 2016), pp. 3–10. [Google Scholar]
  • 2.Wang J. H.-C., “Mechanobiology of tendon,” J. Biomech. 39(9), 1563–1582 (2006). 10.1016/j.jbiomech.2005.05.011 [DOI] [PubMed] [Google Scholar]
  • 3.Bordoni B., Black A. C., and Varacallo M. A., “Anatomy, tendons,” in StatPearls (StatPearls Publishing, Treasure Island, FL, 2025). [PubMed] [Google Scholar]
  • 4.Benjamin M., “14 - The structure of tendons and ligaments,” in Regenerative Medicine and Biomaterials for the Repair of Connective Tissues, edited by Archer C. and Ralphs J. (Woodhead Publishing, 2010), pp. 351–374. [Google Scholar]
  • 5.Buschmann J. and Bürgisser G. M., “1 - Structure and function of tendon and ligament tissues,” in Biomechanics of Tendons and Ligaments, edited by Buschmann J. and Bürgisser G. Meier (Woodhead Publishing, 2017), pp. 3–29. [Google Scholar]
  • 6.Kwan K. Y. C., Ng K. W. K., Rao Y., Zhu C., Qi S., Tuan R. S., Ker D. F. E., and Wang D. M., “Effect of aging on tendon biology, biomechanics and implications for treatment approaches,” Int. J. Mol. Sci. 24(20), 15183 (2023). 10.3390/ijms242015183 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Fouda M. B., Thankam F. G., Dilisio M. F., and Agrawal D. K., “Alterations in tendon microenvironment in response to mechanical load: Potential molecular targets for treatment strategies,” Am. J. Transl. Res. 9(10), 4341–4360 (2017); available at https://pmc.ncbi.nlm.nih.gov/articles/PMC5666046/. [PMC free article] [PubMed] [Google Scholar]
  • 8.Magnusson S. P., Langberg H., and Kjaer M., “The pathogenesis of tendinopathy: Balancing the response to loading,” Nat. Rev. Rheumatol. 6(5), 262–268 (2010). 10.1038/nrrheum.2010.43 [DOI] [PubMed] [Google Scholar]
  • 9.Xu Y. and Murrell G. A. C., “The basic science of tendinopathy,” Clin. Orthop. Relat. Res. 466(7), 1528–1538 (2008). 10.1007/s11999-008-0286-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Allis C. D. and Jenuwein T., “The molecular hallmarks of epigenetic control,” Nat. Rev. Genet. 17(8), 487–500 (2016). 10.1038/nrg.2016.59 [DOI] [PubMed] [Google Scholar]
  • 11.Allfrey V. G., Faulkner R., and Mirsky A. E., “Acetylation and methylation of histones and their possible role in the regulation of RNA synthesis,” Proc. Natl. Acad. Sci. U. S. A. 51(5), 786–794 (1964). 10.1073/pnas.51.5.786 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Stowers R. and Chaudhuri O., “Epigenetic regulation of mechanotransduction,” Nat. Biomed. Eng. 5(1), 8–10 (2021). 10.1038/s41551-020-00678-6 [DOI] [PubMed] [Google Scholar]
  • 13.Atlasi Y. and Stunnenberg H. G., “The interplay of epigenetic marks during stem cell differentiation and development,” Nat. Rev. Genet. 18(11), 643–658 (2017). 10.1038/nrg.2017.57 [DOI] [PubMed] [Google Scholar]
  • 14.Skinner M. K., “Role of epigenetics in developmental biology and transgenerational inheritance,” Birth Defects Res., Part C 93(1), 51–55 (2011). 10.1002/bdrc.20199 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Czermin B., Melfi R., McCabe D., Seitz V., Imhof A., and Pirrotta V., “Drosophila enhancer of Zeste/ESC complexes have a histone H3 methyltransferase activity that marks chromosomal Polycomb sites,” Cell 111(2), 185–196 (2002). 10.1016/S0092-8674(02)00975-3 [DOI] [PubMed] [Google Scholar]
  • 16.Müller J., Hart C. M., Francis N. J., Vargas M. L., Sengupta A., Wild B., Miller E. L., O'Connor M. B., Kingston R. E., and Simon J. A., “Histone methyltransferase activity of a Drosophila Polycomb group repressor complex,” Cell 111(2), 197–208 (2002). 10.1016/S0092-8674(02)00976-5 [DOI] [PubMed] [Google Scholar]
  • 17.Kuzmichev A., Nishioka K., Erdjument-Bromage H., Tempst P., and Reinberg D., “Histone methyltransferase activity associated with a human multiprotein complex containing the Enhancer of Zeste protein,” Genes Dev. 16(22), 2893–2905 (2002). 10.1101/gad.1035902 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Cao R., Wang L., Wang H., Xia L., Erdjument-Bromage H., Tempst P., Jones R. S., and Zhang Y., “Role of histone H3 lysine 27 methylation in Polycomb-group silencing,” Science 298(5595), 1039–1043 (2002). 10.1126/science.1076997 [DOI] [PubMed] [Google Scholar]
  • 19.Sun D., Luo M., Jeong M., Rodriguez B., Xia Z., Hannah R., Wang H., Le T., Faull K. F., Chen R., Gu H., Bock C., Meissner A., Göttgens B., Darlington G. J., Li W., and Goodell M. A., “Epigenomic profiling of young and aged HSCs reveals concerted changes during aging that reinforce self-renewal,” Cell Stem Cell 14(5), 673–688 (2014). 10.1016/j.stem.2014.03.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Krogan N. J., Dover J., Khorrami S., Greenblatt J. F., Schneider J., Johnston M., and Shilatifard A., “COMPASS, a histone H3 (Lysine 4) methyltransferase required for telomeric silencing of gene expression,” J. Biol. Chem. 277(13), 10753–10755 (2002). 10.1074/jbc.C200023200 [DOI] [PubMed] [Google Scholar]
  • 21.Milne T. A., Briggs S. D., Brock H. W., Martin M. E., Gibbs D., Allis C. D., and Hess J. L., “MLL targets SET domain methyltransferase activity to Hox gene promoters,” Mol. Cell 10(5), 1107–1117 (2002). 10.1016/S1097-2765(02)00741-4 [DOI] [PubMed] [Google Scholar]
  • 22.Nakamura T., Mori T., Tada S., Krajewski W., Rozovskaia T., Wassell R., Dubois G., Mazo A., Croce C. M., and Canaani E., “ALL-1 is a histone methyltransferase that assembles a supercomplex of proteins involved in transcriptional regulation,” Mol. Cell 10(5), 1119–1128 (2002). 10.1016/S1097-2765(02)00740-2 [DOI] [PubMed] [Google Scholar]
  • 23.Yokoyama A., Wang Z., Wysocka J., Sanyal M., Aufiero D. J., Kitabayashi I., Herr W., and Cleary M. L., “Leukemia proto-oncoprotein MLL forms a SET1-like histone methyltransferase complex with menin to regulate Hox gene expression,” Mol. Cell. Biol. 24(13), 5639–5649 (2004). 10.1128/MCB.24.13.5639-5649.2004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Dou Y., Milne T. A., Ruthenburg A. J., Lee S., Lee J. W., Verdine G. L., Allis C. D., and Roeder R. G., “Regulation of MLL1 H3K4 methyltransferase activity by its core components,” Nat. Struct. Mol. Biol. 13(8), 713–719 (2006). 10.1038/nsmb1128 [DOI] [PubMed] [Google Scholar]
  • 25.Benayoun B. A., Pollina E. A., Ucar D., Mahmoudi S., Karra K., Wong E. D., Devarajan K., Daugherty A. C., Kundaje A. B., Mancini E., Hitz B. C., Gupta R., Rando T. A., Baker J. C., Snyder M. P., Cherry J. M., and Brunet A., “H3K4me3 breadth is linked to cell identity and transcriptional consistency,” Cell 158(3), 673–688 (2014). 10.1016/j.cell.2014.06.027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Phillip J. M., Aifuwa I., Walston J., and Wirtz D., “The mechanobiology of aging,” Annu. Rev. Biomed. Eng. 17, 113–141 (2015). 10.1146/annurev-bioeng-071114-040829 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Childs B. G., Durik M., Baker D. J., and van Deursen J. M., “Cellular senescence in aging and age-related disease: From mechanisms to therapy,” Nat. Med. 21(12), 1424–1435 (2015). 10.1038/nm.4000 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Moraga A., Pradillo J. M., García-Culebras A., Palma-Tortosa S., Ballesteros I., Hernández-Jiménez M., Moro M. A., and Lizasoain I., “Aging increases microglial proliferation, delays cell migration, and decreases cortical neurogenesis after focal cerebral ischemia,” J. Neuroinflammation 12(1), 87 (2015). 10.1186/s12974-015-0314-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Kraning-Rush C. M., Carey S. P., Califano J. P., Smith B. N., and Reinhart-King C. A., “The role of the cytoskeleton in cellular force generation in 2D and 3D environments,” Phys. Biol. 8(1), 015009 (2011). 10.1088/1478-3975/8/1/015009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Reinhart-King C. A., “Chapter 3 endothelial cell adhesion and migration,” in Methods in Enzymology (Academic Press, 2008), pp. 45–64. [DOI] [PubMed] [Google Scholar]
  • 31.Lemmon C. A., Chen C. S., and Romer L. H., “Cell traction forces direct fibronectin matrix assembly,” Biophys. J. 96(2), 729–738 (2009). 10.1016/j.bpj.2008.10.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Bannister A. J. and Kouzarides T., “Regulation of chromatin by histone modifications,” Cell Res. 21(3), 381–395 (2011). 10.1038/cr.2011.22 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Liu X., Wang C., Liu W., Li J., Li C., Kou X., Chen J., Zhao Y., Gao H., Wang H., Zhang Y., Gao Y., and Gao S., “Distinct features of H3K4me3 and H3K27me3 chromatin domains in pre-implantation embryos,” Nature 537(7621), 558–562 (2016). 10.1038/nature19362 [DOI] [PubMed] [Google Scholar]
  • 34.Yapp C., Carr A. J., Price A., Oppermann U., and Snelling S. J. B., “H3K27me3 demethylases regulate in vitro chondrogenesis and chondrocyte activity in osteoarthritis,” Arthritis Res. Ther. 18, 158 (2016). 10.1186/s13075-016-1053-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Zhao Y., Hu J., Wu J., and Li Z., “ChIP-seq profiling of H3K4me3 and H3K27me3 in an invasive insect, Bactrocera dorsalis,” Front. Genet. 14, 1108104 (2023). 10.3389/fgene.2023.1108104 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Bernstein B. E., Kamal M., Lindblad-Toh K., Bekiranov S., Bailey D. K., Huebert D. J., McMahon S., Karlsson E. K., Kulbokas E. J., Gingeras T. R., Schreiber S. L., and Lander E. S., “Genomic maps and comparative analysis of histone modifications in human and mouse,” Cell 120(2), 169–181 (2005). 10.1016/j.cell.2005.01.001 [DOI] [PubMed] [Google Scholar]
  • 37.Margueron R., Li G., Sarma K., Blais A., Zavadil J., Woodcock C. L., Dynlacht B. D., and Reinberg D., “Ezh1 and Ezh2 maintain repressive chromatin through different mechanisms,” Mol. Cell 32(4), 503–518 (2008). 10.1016/j.molcel.2008.11.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Grossniklaus U. and Paro R., “Transcriptional silencing by polycomb-group proteins,” Cold Spring Harb Perspect. Biol. 6(11), a019331 (2014). 10.1101/cshperspect.a019331 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Aranda S., Mas G., and Di Croce L., “Regulation of gene transcription by Polycomb proteins,” Sci. Adv. 1(11), e1500737 (2015). 10.1126/sciadv.1500737 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Conway E., Healy E., and Bracken A. P., “PRC2 mediated H3K27 methylations in cellular identity and cancer,” Curr. Opin. Cell Biol. 37, 42–48 (2015). 10.1016/j.ceb.2015.10.003 [DOI] [PubMed] [Google Scholar]
  • 41.Lachumanan R., Armugam A., Tan C.-H., and Jeyaseelan K., “Structure and organization of the cardiotoxin genes in Naja sputatrix1,” FEBS Lett. 433(1), 119–124 (1998). 10.1016/S0014-5793(98)00894-1 [DOI] [PubMed] [Google Scholar]
  • 42.Bikkavilli R. K., Avasarala S., Van Scoyk M., Arcaroli J., Brzezinski C., Zhang W., Edwards M. G., Rathinam M. K. K., Zhou T., Tauler J., Borowicz S., Lussier Y. A., Parr B. A., Cool C. D., and Winn R. A., “Wnt7a is a novel inducer of β-catenin-independent tumor-suppressive cellular senescence in lung cancer,” Oncogene 34(42), 5317–5328 (2015). 10.1038/onc.2015.2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Mahoney J. A., Ntolosi B., DaSilva R. P., Gordon S., and McKnight A. J., “Cloning and characterization of CPVL, a novel serine carboxypeptidase, from human macrophages,” Genomics 72(3), 243–251 (2001). 10.1006/geno.2000.6484 [DOI] [PubMed] [Google Scholar]
  • 44.Trimpont M. V., Schalk A. M., Visser Y. D., Nguyen H. A., Reunes L., Vandemeulebroecke K., Peeters E., Su Y., Lee H., Lorenzi P. L., Chan W.-K., Mondelaers V., Moerloose B. D., Lammens T., Goossens S., Vlierberghe P. V., and Lavie A., “In vivo stabilization of a less toxic asparaginase variant leads to a durable antitumor response in acute leukemia,” Haematologica 108(2), 409–419 (2022). 10.3324/haematol.2022.281390 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Terao M., Barzago M. M., Kurosaki M., Fratelli M., Bolis M., Borsotti A., Bigini P., Micotti E., Carli M., Invernizzi R. W., Bagnati R., Passoni A., Pastorelli R., Brunelli L., Toschi I., Cesari V., Sanoh S., and Garattini E., “Mouse aldehyde-oxidase-4 controls diurnal rhythms, fat deposition and locomotor activity,” Sci. Rep. 6(1), 30343 (2016). 10.1038/srep30343 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Heo S.-J., Thorpe S. D., Driscoll T. P., Duncan R. L., Lee D. A., and Mauck R. L., “Biophysical regulation of chromatin architecture instills a mechanical memory in mesenchymal stem cells,” Sci. Rep. 5(1), 16895 (2015). 10.1038/srep16895 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Heo S.-J., Thakur S., Chen X., Loebel C., Xia B., McBeath R., Burdick J. A., Shenoy V. B., Mauck R. L., and Lakadamyali M., “Aberrant chromatin reorganization in cells from diseased fibrous connective tissue in response to altered chemomechanical cues,” Nat. Biomed. Eng. 7(2), 177–191 (2022). 10.1038/s41551-022-00910-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Masuzawa R., Rosa Flete H. K., Shimizu J., and Kawano F., “Age-related histone H3.3 accumulation associates with a repressive chromatin in mouse tibialis anterior muscle,” J. Physiol. Sci. 74(1), 41 (2024). 10.1186/s12576-024-00935-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Turner D. C., Gorski P. P., Maasar M. F., Seaborne R. A., Baumert P., Brown A. D., Kitchen M. O., Erskine R. M., Dos-Remedios I., Voisin S., Eynon N., Sultanov R. I., Borisov O. V., Larin A. K., Semenova E. A., Popov D. V., Generozov E. V., Stewart C. E., Drust B., Owens D. J., Ahmetov I. I., and Sharples A. P., “DNA methylation across the genome in aged human skeletal muscle tissue and muscle-derived cells: The role of HOX genes and physical activity,” Sci. Rep. 10(1), 15360 (2020). 10.1038/s41598-020-72730-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.de Haan G. and Lazare S. S., “Aging of hematopoietic stem cells,” Blood 131(5), 479–487 (2018). 10.1182/blood-2017-06-746412 [DOI] [PubMed] [Google Scholar]
  • 51.Wysocka J., Swigut T., Xiao H., Milne T. A., Kwon S. Y., Landry J., Kauer M., Tackett A. J., Chait B. T., Badenhorst P., Wu C., and Allis C. D., “A PHD finger of NURF couples histone H3 lysine 4 trimethylation with chromatin remodelling,” Nature 442(7098), 86–90 (2006). 10.1038/nature04815 [DOI] [PubMed] [Google Scholar]
  • 52.Subramanian A., Kanzaki L. F., Galloway J. L., and Schilling T. F., “Mechanical force regulates tendon extracellular matrix organization and tenocyte morphogenesis through TGFbeta signaling,” eLife 7, e38069 (2018). 10.7554/eLife.38069 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Ng M. R., Besser A., Brugge J. S., and Danuser G., “Mapping the dynamics of force transduction at cell–cell junctions of epithelial clusters,” eLife 3, e03282 (2014). 10.7554/eLife.03282 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Maeda T., Sakabe T., Sunaga A., Sakai K., Rivera A. L., Keene D. R., Sasaki T., Stavnezer E., Iannotti J., Schweitzer R., Ilic D., Baskaran H., and Sakai T., “Conversion of mechanical force into TGF-β-mediated biochemical signals,” Curr. Biol. 21(11), 933–941 (2011). 10.1016/j.cub.2011.04.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Dyment N. A., Jiang X., Chen L., Hong S.-H., Adams D. J., Ackert-Bicknell C., Shin D.-G., and Rowe D. W., “High-throughput, multi-image cryohistology of mineralized tissues,” J. Visualized Exp.: JoVE 115, 54468 (2016). 10.3791/54468 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Koo B., Lee Y.-J., Park N. R., Heo S. C., Hudson D. M., Fernandes A. A., Friday C. S., Hast M. W., Corr D. T., Keene D. R., Tufa S. F., Dyment N. A., and Joeng K. S., “Characterization of TGFβ1-induced tendon-like structure in the scaffold-free three-dimensional tendon cell culture system,” Sci. Rep. 14(1), 9495 (2024). 10.1038/s41598-024-60221-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Birch H. L., “Tendon matrix composition and turnover in relation to functional requirements,” Int. J. Exp. Pathol. 88(4), 241–248 (2007). 10.1111/j.1365-2613.2007.00552.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Choi R., Smith M., Clarke E., and Little C., “Cellular, matrix, and mechano-biological differences in load-bearing versus positional tendons throughout development and aging: A narrative review,” Connect. Tissue Res. 59(5), 483–494 (2018). 10.1080/03008207.2018.1504929 [DOI] [PubMed] [Google Scholar]
  • 59.Bautista C. A., Srikumar A., Tichy E. D., Qian G., Jiang X., Qin L., Mourkioti F., and Dyment N. A., “CD206+ tendon resident macrophages and their potential crosstalk with fibroblasts and the ECM during tendon growth and maturation,” Front. Physiol. 14, 1122348 (2023). 10.3389/fphys.2023.1122348 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Disser N. P., Ghahramani G. C., Swanson J. B., Wada S., Chao M. L., Rodeo S. A., Oliver D. J., and Mendias C. L., “Widespread diversity in the transcriptomes of functionally divergent limb tendons,” J. Physiol. 598(8), 1537–1550 (2020). 10.1113/JP279646 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Driscoll T. P., Cosgrove B. D., Heo S.-J., Shurden Z. E., and Mauck R. L., “Cytoskeletal to nuclear strain transfer regulates YAP signaling in mesenchymal stem cells,” Biophys. J. 108(12), 2783–2793 (2015). 10.1016/j.bpj.2015.05.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.rHeo S.-J., Song K. H., Thakur S., Miller L. M., Cao X., Peredo A. P., Seiber B. N., Qu F., Driscoll T. P., Shenoy V. B., Lakadamyali M., Burdick J. A., and Mauck R. L., “Nuclear softening expedites interstitial cell migration in fibrous networks and dense connective tissues,” Sci. Adv. 6(25), eaax5083 (2020). 10.1126/sciadv.aax5083 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Cosgrove B. D., Mui K. L., Driscoll T. P., Caliari S. R., Mehta K. D., Assoian R. K., Burdick J. A., and Mauck R. L., “N-cadherin adhesive interactions modulate matrix mechanosensing and fate commitment of mesenchymal stem cells,” Nat. Mater. 15(12), 1297–1306 (2016). 10.1038/nmat4725 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Ricci M. A., Manzo C., García-Parajo M. F., Lakadamyali M., and Cosma M. P., “Chromatin fibers are formed by heterogeneous groups of nucleosomes in vivo,” Cell 160(6), 1145–1158 (2015). 10.1016/j.cell.2015.01.054 [DOI] [PubMed] [Google Scholar]
  • 65.Otterstrom J., Castells-Garcia A., Vicario C., Gomez-Garcia P. A., Cosma M. P., and Lakadamyali M., “Super-resolution microscopy reveals how histone tail acetylation affects DNA compaction within nucleosomes in vivo,” Nucl. Acids Res. 47(16), 8470–8484 (2019). 10.1093/nar/gkz593 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Dobin A., Davis C. A., Schlesinger F., Drenkow J., Zaleski C., Jha S., Batut P., Chaisson M., and Gingeras T. R., “STAR: Ultrafast universal RNA-seq aligner,” Bioinformatics 29(1), 15–21 (2013). 10.1093/bioinformatics/bts635 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Liao Y., Smyth G. K., and Shi W., “featureCounts: An efficient general purpose program for assigning sequence reads to genomic features,” Bioinformatics 30(7), 923–930 (2014). 10.1093/bioinformatics/btt656 [DOI] [PubMed] [Google Scholar]
  • 68.Love M. I., Huber W., and Anders S., “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2,” Genome Biol. 15(12), 550 (2014). 10.1186/s13059-014-0550-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Dataset: Blighe K., Rana S., and Lewis M. (2018). “EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling,” Github. https://github.com/kevinblighe/EnhancedVolcano
  • 70.Yu G., Wang L.-G., Han Y., and He Q.-Y., “clusterProfiler: An R package for comparing biological themes among gene clusters,” OMICS: J. Integr. Biol. 16(5), 284–287 (2012). 10.1089/omi.2011.0118 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Bolger A. M., Lohse M., and Usadel B., “Trimmomatic: A flexible trimmer for Illumina sequence data,” Bioinformatics 30(15), 2114–2120 (2014). 10.1093/bioinformatics/btu170 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Langmead B. and Salzberg S. L., “Fast gapped-read alignment with Bowtie 2,” Nat. Methods 9(4), 357–359 (2012). 10.1038/nmeth.1923 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Li H., Handsaker B., Wysoker A., Fennell T., Ruan J., Homer N., Marth G., Abecasis G., Durbin R., and 1000 Genome Project Data Processing Subgroup, “The sequence alignment/map format and SAMtools,” Bioinformatics 25(16), 2078–2079 (2009). 10.1093/bioinformatics/btp352 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Zhang Y., Liu T., Meyer C. A., Eeckhoute J., Johnson D. S., Bernstein B. E., Nusbaum C., Myers R. M., Brown M., Li W., and Liu X. S., “Model-based analysis of ChIP-Seq (MACS),” Genome Biol. 9(9), R137 (2008). 10.1186/gb-2008-9-9-r137 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Yu G., Wang L.-G., and He Q.-Y., “ChIPseeker: An R/Bioconductor package for ChIP peak annotation, comparison and visualization,” Bioinformatics 31(14), 2382–2383 (2015). 10.1093/bioinformatics/btv145 [DOI] [PubMed] [Google Scholar]
  • 76.Robinson J. T., Thorvaldsdóttir H., Winckler W., Guttman M., Lander E. S., Getz G., and Mesirov J. P., “Integrative genomics viewer,” Nat. Biotechnol. 29(1), 24–26 (2011). 10.1038/nbt.1754 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Wang H. and Helin K., “Roles of H3K4 methylation in biology and disease,” Trends Cell Biol. 35(2), 115–128 (2025). 10.1016/j.tcb.2024.06.001 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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