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. Author manuscript; available in PMC: 2023 Nov 1.
Published in final edited form as: Orthod Craniofac Res. 2023 Apr 5;26(4):632–641. doi: 10.1111/ocr.12658

Metabolomic signatures distinguish extracellular vesicles from osteoclasts and odontoclasts

Wellington J Rody Jr 1, Nathan G Reuter 2, Shannen E Brooks 2, Lina I Hammadi 2, Macey L Martin 2, Joy G Cagmat 3, Timothy J Garrett 3, L Shannon Holliday 2,4
PMCID: PMC10542960  NIHMSID: NIHMS1898945  PMID: 36997279

Abstract

Aims:

Pathological dental root resorption and alveolar bone loss are often detected only after irreversible damage. Biomarkers in the gingival crevicular fluid or saliva could provide a means for early detection; however, such biomarkers have proven elusive. We hypothesize that a multiomic approach might yield reliable diagnostic signatures for root resorption and alveolar bone loss. Previously, we showed that extracellular vesicles (EVs) from osteoclasts and odontoclasts differ in their protein composition. In this study, we investigated the metabolome of EVs from osteoclasts, odontoclasts and clasts (non-resorbing clastic cells).

Materials and Methods:

Mouse haematopoietic precursors were cultured on dentine, bone or plastic, in the presence of recombinant RANKL and CSF-1 to trigger differentiation along the clastic line. On Day 7, the cells were fixed and the differentiation state and resorptive status of the clastic cells were confirmed. EVs were isolated from the conditioned media on Day 7 and characterized by nanoparticle tracking and electron microscopy to ensure quality. Global metabolomic profiling was performed using a Thermo Q-Exactive Orbitrap mass spectrometer with a Dionex UHPLC and autosampler.

Results:

We identified 978 metabolites in clastic EVs. Of those, 79 are potential biomarkers with Variable Interdependent Parameters scores of 2 or greater. Known metabolites cytidine, isocytosine, thymine, succinate and citrulline were found at statistically higher levels in EVs from odontoclasts compared with osteoclasts.

Conclusion:

We conclude that numerous metabolites found in odontoclast EVs differ from those in osteoclast EVs, and thus represent potential biomarkers for root resorption and periodontal tissue destruction.

Keywords: alveolar bone loss, biomarker, exosome, microvesicle, root resorption

1 ∣. INTRODUCTION

Clastic cells are multinuclear giant cells responsible for the breakdown of mineralized tissues, including bone and tooth dentine. Osteoclasts are the primary bone-resorbing cells, while odontoclasts are the primary dentine-resorbing cells.1,2 It is likely osteoclasts and odontoclasts differentiate from the same haematopoietic precursor cells and develop distinguishing characteristics due to their interaction with the matrix being resorbed.2-4 From a clinical perspective, understanding the phenotypic differences between osteoclasts and odontoclasts is imperative for the management of a host of oral diseases such as periodontitis, pathologic dental root resorption and tooth ankylosis.

As previously,5 we have used the term clasts to refer to differentiated clastic cells on plastic. These cells do not form a resorption compartment (actin ring, ruffled membrane).1 We refer to clastic cells that are in the process of resorbing bone as osteoclasts, and clastic cells resorbing dentine as odontoclasts. Researchers have often used dentine slices to test ‘osteoclast’ activity, but as we show in this study and our previous work,5 EVs from these osteoclasts and odontoclasts are significantly different.

There have been several recent single cell sequencing studies that suggest immune/myeloid cells in the bone marrow in different skeletal site have intrinsic differences.6,7 In this study we used marrow from long bones; it is possible that haematopoietic cells derived from marrow from other skeletal sites may differentiate differently.

Extracellular vesicles (EVs) are cell-derived vesicles that range from 30 to 150 nm in diameter and contain proteins, lipids, metabolites and ribonucleic acids (RNAs). EVs include exosomes, which are released when multivesicular bodies fuse with the plasma membrane and microvesicles, which bud directly from the plasma membrane.8 In most cases, it is impossible to distinguish the two in bulk isolates, and therefore the mixes are called EVs.9 They are present in biological fluids, including blood, urine, saliva, gingival crevicular fluid (GCF), mucus, breast milk and bile.10-13 As a result, EVs may serve as a useful source of biomarkers for specific pathological conditions based on their known biological role as signalling molecules, as well as the tissue specificity of their composition.11,12,14-16

Previous work showed that the regulatory activity and composition of EVs distinguish between preosteoclasts and osteoclasts,17,18 and osteoclasts and odontoclasts in vitro.19 Proteomic studies also confirmed that, in vitro, osteoclasts and odontoclasts produce EVs with significant differences in their protein cargo.5

Despite extensive efforts, diagnostic strategies for dental conditions depending on oral fluids have not been sufficiently reliable to be useful in the clinic.20-22 Thus far, strategies have relied upon a single type of marker, usually proteins or microRNAs. As appropriate technologies have developed, low molecular weight metabolites have gained interest as biomarkers, and metabolomics has become a potentially very powerful phenotyping tool. In addition, it is thought that the use of overall signatures of markers from different omics platforms (proteins, nucleic acids, metabolites and lipids) might provide more accurate diagnostic tools than those relying on a single biomarker, or even one specific type of biomarker.23,24

In the current study, we examined whether the metabolomic signature of EVs distinguishes osteoclasts from odontoclasts in vitro. This represents a proof-in-principle test of whether metabolite signatures of EVs released by clastic cells might be used to foster the development of liquid biopsy assays for oral diseases. Our ultimate goal in future experiments is to incorporate metabolites with proteins and other components of EVs, as features to be used by artificial intelligence (AI) in a multiomic strategy that makes use of biomarker signatures to develop a robust diagnostic algorithm.23,25 This may allow early diagnosis of pathological root resorption and alveolar bone loss from samples of GCF or saliva.

2 ∣. MATERIALS AND METHODS

2.1 ∣. Experimental design and cell culture protocol

The use of animals was approved by the Institutional Animal Care and Usage Committee (IACUC) of the University of Florida (protocol number, 2012075960) and Stony Brook University (protocol number 12828799). Clastic cell precursors were isolated from the bone marrow of 4 to 6-week-old wild-type C57Bl/6 mice by flushing with alpha-MEM (2.5 mL/bone).26 The mononuclear cells were then plated at a density of 0.5×106 cells/well on bovine cortical bone slices (Publix), dentine discs (Immunodiagnostic Systems, Gaithersburg, MD), or empty plastic cell culture wells (Figure 1A) and cultured for seven days with 10% exosome-depleted fetal bovine serum (Exo-FBS, System Biosciences).5 To induce osteoclastogenesis, mononuclear cells were supplemented with recombinant mouse macrophage colony-stimulating factor (M-CSF; 20 ng/mL: R&D Systems.) and recombinant soluble mouse glutathione S transferase-RANK- Ligand (RANKL) fusion protein (5 ng/mL.).5 Media was changed on Day 3, and the viability of the cells was confirmed by visual inspection using a light microscope. Conditioned media was collected on Day 7 for the isolation of EVs. At the end of the cell culture period, clastic cell formation was assessed by staining for tartrate-resistant acid phosphatase (TRAP) using a leukocyte phosphatase kit (Sigma-Aldrich) as per the manufacturer's instruction. Resorption status was determined by using actin ring formation, detected by rhodamine-tagged phalloidin as a surrogate marker of resorption as previously described.27 Experiments were performed in triplicate to reduce random error and experimental bias.

FIGURE 1.

FIGURE 1

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Formation of multinuclear clastic cells with resorbing potential and characterization of EVs. (A–C) Presence of tartrate-resistant acid phosphatase (TRAP) positive cells at the end of the cell culture period. (A) clasts on plastic; (B) osteoclasts on bone slices; (C) odontoclasts on dentine. (A′-C′) Actin belt (A′) of clasts and Actin rings of osteoclasts (B′) and odontoclasts (C′). Scale bar = 20 μm for all images.

2.2 ∣. EV isolation and quantitation

Cell-conditioned media was removed from the wells, pooled in sterile 15 mL conical tubes and centrifuged at 3000g for 30 min at room temperature, followed by 150 000 g for 2 hours. EVs were quantified in 10 μL of the resuspended pellet obtained from each matrix type by nanoparticle tracking using a Nanosight LM50 (Malvern).26

2.3 ∣. Transmission electron microscopy (TEM)

Extracellular vesicles were visualized using transmission electron microscopy as described previously.17 Briefly, EV pellets were resuspended in 2% paraformaldehyde and placed on a copper grid coated with a carbon support film. The grids were then stained with 1% v/v uranyl acetate in ddH2O, and the samples were examined immediately using a Hitachi 7600 transmission electron microscope operated at 80 kV.

2.4 ∣. Immunoblots

Extracellular vesicles or whole cell samples were subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred to Immobilon FL (EMD Millipore). Blots were probed with antibodies to vacuolar H+-ATPase,17 anti-Actin (Sigma, cat# ZMS1004), anti-CD81 (eBioscience clone, Eat-2) and anti-GP96 (Life Technologies, 36-2600). These were then probed with horse-radish peroxidase secondary antibodies (Sigma). We used a chemiluminescent substrate (ThermoFisher; Super Signal West Pico) and a BioRad ChemiDoc MP (BioRad) to detect bands. The Optimal Autoexposure setting was used to acquire images. The raw chemiluminescent data were minimally processed using brightness and contrast controllers equally over the entirety of each blot in Adobe Photoshop for final figures.

2.5 ∣. Metabolite profiling

Global metabolomics profiling was performed on a Thermo Q-Exactive Orbitrap mass spectrometer with Dionex UHPLC and autosampler.28 Samples from triplicate cultures were analysed in positive- and negative-heated electrospray ionization with a mass resolution of 35 000 at m/z 200 as separate injections. Separation was achieved on an ACE 18-pfp 100×2.1 mm, 2μm column with mobile phase A as 0.1% formic acid in water and mobile phase B as acetonitrile. This is a polar-embedded stationary phase that provides comprehensive coverage but does have some limitation in the coverage of very polar species. The flow rate was 350 μL/min with a column temperature of 25°C. 4μL was injected for negative ions and 2 μL for positive ions.

2.6 ∣. Metabolomics Analysis

Data from positive and negative ion modes were separately subjected to statistical analyses. All subsequent data analyses were normalized to the sum of metabolites for each sample. MZmine (freeware)29 was used to identify features, deisotope, align features and perform gap filling to fill in any features that may have been missed in the first alignment algorithm. All adducts and complexes were identified and removed from the data set. All settings including the MZmine file can be provided if requested. MZmine is a free metabolomic data processing program. The data tables consist of several columns that are described in the next section. The data were searched against the internal retention time metabolite library of 1400 compounds for identification established by the Southeast Center for Integrated Metabolomics (SECIM) at University of Florida.

2.7 ∣. Data processing and quality control

Data were quality-checked by examining the performance of spiked internal standards in all samples throughout the sequence. Standards confirmed reproducibility in the analysis showing a relative standard deviation < 10%. Data file format conversion was performed using RawConverter (metabolite data).30 MZmine 2 was utilized for peak picking, chromatographic alignment, and feature identification (metabolites) using our custom internal library by m/z-retention time matching.31 Non-detected species (intensity = 0) were replaced with half the minimum value in the data set for statistical purposes. A feature-by-feature signal intensity filtering algorithm was conducted to remove features with significant signal contribution from the background.31

2.8 ∣. Statistical Analysis

MetaboAnalyst 4.032 was used to perform statistical analysis with the following parameters: (1) peak intensity data, (2) samples in rows unpaired, (3) missing value estimation used to replace by a small value (half of the minimum positive value in the original data, none of the features were removed in this step), (4) normalized to sum (to correct the instrumental and the technical variation), (5) data transformed using log transformation, and (6) autoscaled (to allow a more direct comparison between features of greatly varying intensities). Principal component analysis (PCA), an unsupervised statistical model, was employed to visualize variance and emphasize variation in both metabolomic and lipidomic analysis. Heatmaps (hierarchical clustering analysis based on Student's t-test) were generated using Python 3 Seaborn to visualize metabolite and lipid intensity differences between the two samples generated from both positive and negative ion mode data.

Nanoparticle tracking results are expressed as mean plus/minus Standard Error. Samples were compared by one-way ANOVA with Tukey's modification and Student's t-test using the program GraphPad Prism 5 (GraphPad Software, La Jolla, CA). p-values <.05 were considered significant. The biological relevance of the metabolomics data was studied using univariate and multivariate analysis techniques. Univariate analysis by ANOVA was performed on all data sets (bone, dentine and plastic) in order to identify significant metabolites with p-values <.05. Multivariate analysis was performed using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) with the aim of identifying features contributing most to variation between the biological samples.33

3 ∣. RESULTS

3.1 ∣. Resorbing and non-resorbing clastic cells release EVs in vitro

TRAP-positive multinuclear clastic cells were generated on plastic, bone and dentine (Figure 1A-C). The activation of the cells to resorb dentine (odontoclasts) and bone (osteoclasts) was confirmed by the formation of actin ring structures at the end of the cell culture period (Figure 1D-F). Nanoparticle tracking analysis (Figure 2A-C) and transmission electron microscopy (Figure 2D-F) did not show a significant difference in EV size between the groups studied. Consistent with our previous publication, which used a different EV isolation protocol that involved a commercial precipitation reagent (ExoQuick, Systems Biosciences), we found that clastic cells on plastic produced the highest average number of EVs (9.22×109/ml), followed by bone (6.994×109/ml) and dentine (3.842×109/ml). A panel of three positive markers of clastic EVs (CD81, E-subunit of the vacuolar H+-ATPase and actin) and one negative marker (GP96) was used to confirm purity of EV samples (Figure 3). It was observed that the acti-GP96 antibody reacted only with whole clastic cell extracts.

FIGURE 2.

FIGURE 2

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Characteristics of extracellular vesicles form clasts, osteoclasts and odontoclasts. Nanoparticle tracking data for (A), clasts, (B), osteoclasts and (C), odontoclasts. TEM of EVs for (D) clasts, (E) osteoclasts and (F) odontoclasts. Scale bar = 100 nm for all images.

FIGURE 3.

FIGURE 3

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Immunoblots of EVs show markers of EVs are present and a typical EV contaminant is not detected. 50 μg of protein was separated by SDS-PAGE, blotted to Immobilon P and probed with the antibodies indicated. As a positive control for the anti-GP96 antibody, whole cell extract from osteoclast was probed.

3.2 ∣. Metabolomic composition of EVs derived from clastic cells on plastic, bone and dentine were significantly different

Because metabolites are acids, bases and zwitterionic, analysis was performed in both positive ionization and negative ionization modes in order to get the most coverage of the metabolome. The number of significant metabolites with p-value <.05 are summarized in Table 1. The known top metabolites for each data set are listed in Supplementary Table S1. Over 2000 metabolites were detected in the samples with 60% being identified in positive ion mode and 40% in negative ion mode. There were a total of 1268 features detected from the positive mode and 849 features in the negative mode. Nine hundred seventy-eight metabolites differed significantly when both positive and negative ion modes were considered. Of those, 100 could be identified from a retention time database (Table S1).

TABLE 1.

Known metabolites that differ significantly by ANOVA.

Known Metabolites
More abundant in Odontoclasts compared with Osteoclasts
 Cytidine
 Isocytosine
 Thymine
 Succinate
 Citrulline
 Deoxycytidine
More abundant in Osteoclasts compared with Odontoclasts
 Uridine
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Principal component analysis results show principal component 1 (PC1) and principal component 2 (PC2) describing 59.9% (positive ion mode) and 75.3% (negative ion mode) of the total variance including the unknowns. PCA plots of metabolomics profile based on principal component 1 and principal component 2 from mean intensity values of total detected metabolites are shown (Figure 4A). PLS-DA analysis of the positive and negative mode data sets is shown in Figure 4B. PLS-DA permutation test results yielded observed statistics of p = .98 (POS) and p = .42 (NEG) for two components (Figure 4C). There were 39 (POS) and 40 (NEG) features from PLS-DA that were significant with variable importance in projection (VIP) scores of 2 or greater, which is considered statistically significant.

FIGURE 4.

FIGURE 4

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Overall characteristics and analysis of metabolites detected in clastic EVs. (A) Principal component analysis (PCA) plots of metabolomics profile based on principal component 1 (PC1) and principal component 2 (PC2) from mean intensity values of total detected metabolites from osteoclastic EVs (red circles), odontoclastic EVs (green circles) and clastic EVs (blue circles). The contribution ratios were 59.9% (positive) and 75.3% (negative). (B) Partial least squares-discriminant analysis (PLS-DA) was used to confirm differences between the metabolites for the different sources. Score plots for metabolites from osteoclastic EVs (red circles), odontoclastic EVs (green circles) and clastic EVs (blue circles) are plotted for positive and negative modes. (C) PLS-DA permutation test results yielded observed statistics of p = .98 (POS) and p = .42 (NEG) for 2 components. There were 39 (positive) and 40 (negative) features from PLS-DA that were significant with a VIP score of 2 or greater. These represent new potential biomarkers.

Heat maps of the top 50 molecules in positive (Figure 5A) and negative (Figure 5B) ion modes are based on lowest ANOVA p-values. The clustering of some metabolites between the groups indicates that specific metabolomic signatures may be used to characterize EVs from different substrates (plastic, bone and dentine). The unknown features are represented by numbers with the first number being the mass-to-charge ratio (m/z) and the second the retention time. Several known features were present abundantly in odontoclast EVs compared with osteoclast EVs. These are citrulline, thymine, succinate, glycerophosphochol and deoxycytidine. Conversely, uridine is found at high levels in osteoclasts compared with odontoclasts.

FIGURE 5.

FIGURE 5

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Metabolites differing significantly in EVs from different sources. (A) Positive mode and (B) negative mode heat maps of the top 50 features from ANOVA are shown. Unknown features are represented by numbers representing their mass-to-charge ratio (m/z) and retention times.

4 ∣. DISCUSSION

This study provides the first metabolomic analysis of EVs from clastic cells in vitro. Recent advancements in multiomic technologies have enabled systems-level analysis towards identification of biomarkers reflecting states of human health and disease.23,24,34 It is thought that the use of EV markers or signatures from different omics platforms (proteins, nucleic acids, metabolites and lipids) might provide more accurate diagnostic tools than those relying on a single biomarker, or even a single type of biomarker.35 Previously, we utilized proteomic approaches to seek protein signatures in EVs that distinguish osteoclasts and odontoclasts.5,19 Our eventual goal is to identify biomarkers or biomarker signatures in EVs that can be used to detect pathological root resorption and/or alveolar bone loss in liquid biopsies of gingival crevicular fluid or saliva.34 Such an approach may ultimately rely on artificial intelligence (AI) and machine learning techniques to detect changes in biomarker profiles to discriminate between normal and pathological signatures.

Our data show that the substrate influences the metabolite signature of the EVs released from these osteoclasts, odontoclasts and clasts, which all originate from the same haematopoietic precursors and differentiate in response to exposure to RANKL and CSF-1 and their substrate. That several known, and many unknown metabolites, are found in high levels in EVs from odontoclasts compared with osteoclasts in this in vitro system, supports the hypothesis that metabolites found in EVs may be used as markers to differentiate between the process of bone and dentine resorption. Together, with our previous proteomic analysis comparing clasts, osteoclasts and odontoclasts, these data show that EVs differ between odontoclasts and osteoclasts in both proteins and metabolite signatures, providing a basis for a multiomic approach to diagnosing pathological root resorption. Such an approach may provide signatures that are sufficiently distinct to be detected within the background that is inherent in oral biofluids. To date, this has prevented the clinical use of oral biofluids for dental diagnostics. We anticipate that data from multiomic analysis will be suitable for AI-driven approaches for diagnosing pathological root resorption. Much work must be done to characterize the underlying mechanisms that drive the differences in EVs; however, this information will likely be crucial for selecting the best features for use in AI algorithms. We expect that biomarker signatures, where the features used are mechanistically understood will prove more useful and reliable than signatures developed purely from empirical analysis.

Because most of the metabolites that vary between odontoclast and osteoclast EVs are not identified, our picture is murky. More work will be required to identify these unknown metabolites, which represent possible biomarkers. The known metabolites that are enriched in EVs from odontoclasts included citrulline, which is a non-essential amino acid well known to boost nitric oxide production.36 This may suggest that nitric oxide signalling is differently involved in bone resorption compared with dental root resorption. This idea is consistent with a number of studies showing that nitric oxide signalling has an important role in regulating bone resorption during orthodontic tooth movement.37-38 Further research will be required to test this idea.

In summary, we showed that the metabolites found in EVs released by osteoclasts, odontoclasts and non-resorbing clasts have distinct metabolomic signatures. The enrichment of metabolites in EVs most likely reflects the metabolites found in the cytosol of the source cells. Local metabolites would be expected to be trapped in EVs as either exosomes or microvesicles are formed. Metabolites transferred to a target cell could serve as a signalling mechanism for intercellular communication, although this idea is completely unstudied. For example, EVs enriched in citrulline might support increased local nitric oxide activity. Whatever their origin or signalling role, the fact that EVs from odontoclasts have numerous metabolites that are significantly enriched compared to osteoclasts supports the idea that metabolites in EVs might serve as biomarkers for pathological root resorption.

Currently, no approaches have emerged that allow detection of pathological root resorption from gingival crevicular fluid or saliva in the clinic.39 Our long-term approach seeks to utilize EVs as a source of biomarkers for root resorption, metabolites as biomarkers, and the use of A.I. and multiomics to identify signatures of pathological root resorption. The current study shows that the metabolites released by osteoclasts and odontoclasts differ, and together with our prior studies indicating the protein composition of EVs from osteoclasts and odontoclasts differ,5,19 provides a basis for a multiomic approach. Other groups have studied nucleic acids found in osteoclast EVs, but have not yet compared osteoclast with odontoclasts.40 Although we have thus far relied on an in vitro model, this study provides initial support for the hypothesis that metabolites in EVs differ sufficiently between osteoclasts and odontoclasts to serve as biomarkers to distinguish pathological root resorption from normal bone resorption.

Supplementary Material

Top Known Metabolites (Bone, Dentin, Plastic)

ACKNOWLEDGEMENTS

Research reported in this publication was supported by the National Institute of Dental & Craniofacial Research of the National Institutes of Health under Award Number R03DE027504. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

CONFLICT OF INTEREST STATEMENT

The authors declare they have no conflicts of interest.

SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of 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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Associated Data

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

Supplementary Materials

Top Known Metabolites (Bone, Dentin, Plastic)
NIHMS1898945-supplement-Top_Known_Metabolites__Bone__Dentin__Plastic_.xlsx (16.3KB, xlsx)

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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