Chemokine signals are crucial for enhanced homing and differentiation of circulating osteoclast progenitor cells

Patients with arthritis (RA, n = 129; PsA, n = 53) admitted to Clinical Hospital Center “Sestre Milosrdnice”, Clinical Hospital “Sveti Duh”, and Clinical Hospital Center “Zagreb” were included in the study after approval was obtained from the ethics committees of those institutions and of the University of Zagreb School of Medicine, and informed consent was received from patients. Control subjects (CTRL; n = 110) were admitted to the same institutions for noninflammatory etiology and had no previous history of arthritic or autoimmune diseases. A rheumatology specialist established the diagnosis of RA on the basis of American College of Rheumatology 2010 criteria, whereas PsA was diagnosed according to the Moll and Wright criteria [19, 20]. PB samples were obtained from patients with RA and patients with PsA during routine clinical assessments and processed for analysis. The following data were also collected from patients with arthritis: disease duration (years), patient and physician assessment of disease activity (visual analogue scale [VAS]), patient assessment of pain and fatigue (VAS), tender joint count (TJC; out of 28 joints), swollen joint count (SJC; out of 28 joints), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) level, Disease Activity Score in 28 joints (DAS28) calculated with ESR, Clinical Disease Activity Index (CDAI), treatment received (nonsteroidal anti-inflammatory drugs [NSAIDs], disease-modifying antirheumatic drugs [DMARDs], corticosteroids, and/or biological therapy), levels of rheumatoid factor (RF; determined by turbidimetry), and anticitrullinated protein antibodies (ACPA; determined by enzyme-linked immunosorbent assay [ELISA]). For some analyses, patients with RA were grouped on the basis of their DAS28 into severe (DAS28 > 5.1) and low/moderate cases (DAS28 2.6–5.1) [21‐23]. A cohort of patients with arthritis (n = 24) assigned for therapeutic arthrocentesis of the affected knee was included to obtain SF samples. A small subset of patients (n = 9) scheduled for biological treatment with TNF inhibitors (etanercept 50 mg weekly or adalimumab 40 mg every 2 weeks) was followed by taking PB samples immediately before and at 2, 4, and 6 months after onset of therapy.

Blood samples were obtained between 9:30 and 11:30 a.m. PBMCs and plasma were separated from PB by centrifugation (20 minutes at 600 × g) with Histopaque (Sigma-Aldrich, St. Louis, MO, USA). Aspirated SF samples were separated by sequential centrifugation (5 minutes at 100 × g) into acellular SF supernatants and SF-derived cells (synovial fluid mononuclear cells [SFMCs]). Plasma and SF samples were stored at −20 °C until a cytometric bead array (CBA) or ELISA was performed, whereas PBMCs and SFMCs were immediately used for flow cytometric phenotyping, RNA extraction, cell sorting, osteoclastogenic cultures, and migration assays.

Phenotype characterization of OCPs from PB was performed using an Invitrogen Attune flow cytometer (Thermo Fisher Scientific, Waltham, MA, USA) and analyzed using FlowJo software (FlowJo, LLC, Ashland, OR, USA). Cells were counted in a hemocytometer by trypan blue exclusion and labeled using a mix of commercially available monoclonal antibodies against lymphoid lineage markers (anti-CD3 fluorescein isothiocyanate [FITC] for T lymphocytes, anti-CD19 FITC for B lymphocytes, and anti-CD56 FITC for natural killer cells), myeloid lineage markers (anti-CD11b phycoerythrin [PE]-cyanine 7 [Cy7] and anti-CD14 peridinin-chlorophyll-cyanine 5.5), receptors for OC differentiation factors (receptor for M-CSF [biotinylated anti-CD115], RANK [anti-RANK PE]), and several chemokine receptors (anti-CCR1 allophycocyanin [APC], anti-CCR2 PE, anti-CCR4 APC, anti-CXCR3 APC, anti-CXCR4 PE) (all from eBioscience, San Diego, CA, USA). Suspensions were incubated on ice for 40 minutes, followed by washing in staining medium (PBS/2% FBS). As a second step (for biotinylated anti-CD115), cells were stained with streptavidin coupled to APC-eFluor 780 (Thermo Fisher Scientific) on ice for 40 minutes, followed by washing in staining medium, and afterward they were resuspended in staining medium for analysis. OCPs were defined as part of the monocytic subpopulation bearing the CD3⁻CD19⁻CD56⁻CD11b⁺CD14⁺ phenotype [3, 12]. The subpopulation was then analyzed for expression of CD115 and RANK as well as for subsequent chemokine receptor expression (CCR1, CCR2, CCR4, CXCR3, and CXCR4). Gates for the analysis were defined using unlabeled cells and "fluorescence minus one control".

For fluorescence-activated cell sorting (FACS), PBMCs of CTRL and RA samples were labeled using lymphoid lineage markers (anti-CD3 FITC for T lymphocytes, anti-CD19 FITC for B lymphocytes, anti-CD56 FITC for natural killer cells) and myeloid lineage markers (anti-CD11b APC and anti-CD14 PE-Cy7) and sorted using a BD FACSAria I cell sorter (BD Biosciences, Franklin Lakes, NJ, USA). Briefly, labeled cells were acquired at a speed of approximately 5000 cells/second, and singlets were delineated from the total cell population depicted on forward vs side scatter plots, followed by plotting lymphoid markers vs CD11b. Subsequently, lymphoid marker-negative cells, intermediately positive for CD11b, were gated for CD14 expression. A defined OCP population (CD3⁻CD19⁻CD56⁻CD11b⁺CD14⁺) was sorted in 2-ml collection tubes containing α-minimal essential medium (α-MEM)/20% FBS and used for osteoclastogenic cultures. Sorting purity was determined by reanalysis of fractioned populations and was consistently greater than 99%.

Sorted OCPs were plated at a density of 4 × 10⁴ cells per well in 96-well plates in α-MEM/10% FBS. For the first 3 days of cell culture, 60 ng/ml recombinant human (rh)M-CSF (R&D Systems, Minneapolis, MN, USA) was added to stimulate cell proliferation. The next 8–11 days, the cell culture medium was supplemented with 100 ng/ml rhRANKL (R&D Systems) and 30 ng/ml rhM-CSF to stimulate differentiation into mature OCs. At days 11–13 of culture, OCs were enumerated by staining for tartrate-resistant acid phosphatase (TRAP).

PBMCs were plated at a density of 10 × 10⁴ per well in 48-well plates in α-MEM/10% FBS and cultured overnight with 35 ng/ml M-CSF. Nonadherent cells enriched in OCPs were harvested for further analyses as previously described [12]. Considering the heterogeneity of the PB monocyte/macrophage lineage, the nonadherent fraction obtained after short-term incubation was comprised of a mixture of lymphocytes and immature monocytes. Lymphocytes were mostly removed by subsequent media changes.

A portion of nonadherent cells was used in osteoclastogenic culture additionally treated with chemokines. These cells were plated at a density of 2.5 × 10⁴ cells per well in 96-well plates in α-MEM/10% FBS supplemented by 100 ng/ml rhRANKL and 30 ng/ml rhM-CSF, with the addition of 40 ng/ml CCL2, 10 ng/ml CCL5, 20 ng/ml CXC chemokine ligand 10 (CXCL10) or 50 ng/ml CXCL12 (PeproTech, Rocky Hill, NJ, USA). Assays were performed on PB RA samples (n = 6–12 per chemokine treatment). After 10–11 days, cells were stained with TRAP for OC enumeration.

The remainder of nonadherent cells was used for the Transwell culture system. The cells were plated at a density of 5 × 10⁴ cells per well in 48-well plates in α-MEM/10% FBS with 100 ng/ml RANKL and 30 ng/ml M-CSF. At day 2 of culture, adherent cells from each sample were harvested and seeded into the upper chambers of 24-well Costar Transwell plates (8.0-μm pore size; Corning Inc., Corning, NY, USA) at a density of 10⁴ cells/well in 100 μl of α-MEM/10% FBS, and the lower chambers were filled with 40 ng/ml CCL2, 10 ng/ml CCL5, 20 ng/ml CXCL10, or 50 ng/ml CXCL12 (PeproTech) in 500 μl of α-MEM/10% FBS. After 3-h incubation at 37 °C with 5% CO₂, the upper surface of the membrane was carefully washed with PBS, and the remaining cells were removed with a cotton wool swab. The cells that had migrated to the bottom side of the Transwell membrane inserts were fixed with 4% paraformaldehyde and stained with 4′,6-diamidino-2-phenylindole (DAPI). The migrated cells were counted (two wells per group, five central fields per Transwell) at × 200 magnification using a fluorescence microscope (Axiovert 200; Carl Zeiss AG, Oberkochen, Germany).

The cells that had migrated through the Transwell membrane to the lower chamber of the 24-well plate were cultured for the next 8–9 days in α-MEM/10% FBS supplemented with 100 ng/ml RANKL and 30 ng/ml M-CSF. At the end of the culture period, plates were stained for TRAP activity to determine the number of differentiated OCs.

The number of OCs differentiated from PBMCs or the sorted OCP-enriched population was determined by TRAP staining at the culture endpoint (days 10–13). Osteoclastogenic cultures were grown as previously described. Staining for TRAP was performed using a commercially available set of chemicals according to the manufacturer’s instructions (leukocyte acid phosphatase kit; Sigma-Aldrich). OCs were identified and enumerated as TRAP⁺ cells with two or more nuclei per cell at × 200 magnification by light microscopy using the Axiovert 200 fluorescence microscope.

Vitronectin receptor (VNR) expression and bone resorption assays were performed in parallel with TRAP staining to confirm the identity of OCs. For VNR detection, osteoclastogenic cultures were grown as previously described. At culture days 14–16, cells were fixed in 4% paraformaldehyde for 10 minutes and blocked for 30 minutes in 2% bovine serum albumin/PBS. Monoclonal biotinylated CD51/CD61 (integrin α_vβ₃) antibody (1:80; eBioscience) was incubated for 2 h at room temperature. As a second-step reagent, streptavidin coupled to FITC was incubated for 1 h at room temperature. Cell nuclei were counterstained with DAPI. VNR expression was visualized at × 200 magnification using the Axiovert 200 fluorescence microscope.

For bone resorption assays, cells were plated over the bovine cortical bone slices using the same osteoclastogenic culture conditions as previously described. At culture days 16–18, cells were fixed in 4% paraformaldehyde for 10 minutes at room temperature. Following TRAP staining using a commercially available set of chemicals (leukocyte acid phosphatase kit; Sigma-Aldrich), cells were removed from bone slices by sonication in 0.25 M NH₄OH for 5 minutes. Bone slices were then stained with 1% toluidine blue in 1% borax buffer for 2 minutes to visualize resorption pits at × 200 magnification using an Axiovert 200 light microscope.

CBA was performed using the Human Chemokine CBA Kit (BD Biosciences, Sans Jose, CA, USA) to assess the levels of CCL2, CCL3, CCL4, CCL5, CXCL9, and CXCL10 in PB and SF samples. Plasma and SF supernatants were each diluted 1:4 using the supplied dilution buffer and incubated with capture beads. Each capture bead was coated with a capture antibody specific for each soluble protein and could be identified by its distinct fluorescence signature in two fluorescence channels on the flow cytometer (APC vs APC-Cy7). The samples were then washed and incubated with the detection reagent. The concentration of the bound analyte was determined by the intensity of the PE fluorescence, depending on the amount of bound PE-conjugated antibodies. After another washing step, complexes of capture beads, analytes, and detection reagents were analyzed using Invitrogen Attune flow cytometer. Concentrations of chemokines were determined using FCAP Array version 3.0 software (BD Biosciences), based on standard curves generated by data acquired from provided protein standards.

Plasma concentrations of TNF-α (Quantikine HS ELISA kit; R&D Systems), CXCL12 (Quantikine Immunoassay; R&D Systems), and carboxy-terminal telopeptide of type I collagen (CTX; Immunodiagnostic Systems, The Boldons, UK), as well as SF concentrations of RANKL (Quantikine Immunoassay; R&D Systems), were determined in accordance with the manufacturers’ instructions. Briefly, samples were incubated for 2–3 h at room temperature on monoclonal antibody-precoated plates, washed three times, and incubated for the next 2 h with HRP-conjugated (CXCL12, RANKL) or alkaline phosphatase-conjugated (TNF-α, CTX) specific antibodies. After another three washes, the reactions were visualized with substrate solution (tetramethylbenzidine) (CXCL12, RANKL) or substrate and amplifier solutions (nicotinamide adenine dinucleotide phosphate and amplifier enzymes) (TNF-α, CTX) and then arrested with hydrochloric or sulfuric acid, respectively. Optical density was determined within 15 minutes on a microplate reader (Bio-Rad Laboratories, Hercules, CA, USA) set to an excitation wavelength of 450 nm (CXCL12, RANKL) or 490 nm (TNF-α, CTX).

Data were presented as median with IQR for continuous variables (including clinical scores, laboratory measures, protein concentrations, cell numbers and frequencies) or categorized for categorical variables (including sex, DAS28 subgroup, and treatment). Statistical analysis of the group differences was performed using a nonparametric Mann-Whitney U test (for group-to-group comparisons) or the Kruskal-Wallis test followed by the Mann-Whitney U test (for multiple comparisons). Correlations were done using rank correlation and Spearman’s coefficient ρ with 95% CI. Trend analysis was performed on population frequencies during anti-TNF therapy by using one-way analysis of variance (ANOVA) with polynomial contrast type and testing for linear trends. Multiple logistic regression analysis was performed with the frequency of the PB OCP cells (CD3⁻CD19⁻CD56⁻CD11b⁺CD14⁺) as the dependent variable. Parameters were taken into consideration as independent variables if comparative analysis between CTRL and RA values showed a significant difference. An extensive search was then performed to see which three of the significantly different independent variables resulted in maximal adjusted R ² values, with pairwise exclusion of missing values in the analysis. Statistical analyses were performed using MedCalc for Windows version 9.4.2.0 software (MedCalc Software, Ostend, Belgium). For all experiments, the α-level was set at 0.05.

The demographic and clinical characteristics showed that the majority of patients with RA included in the study had a chronic course of active disease (median DAS28 5.97; IQR 4.99–6.71) (Table 1) with multiple joints affected and enhanced osteoresorption (median CTX 0.38 [IQR 0.26–0.49] pg/ml in RA vs 0.28 [IQR 0.21–0.32] pg/ml in CTRL). The majority of patients were seropositive with elevated inflammatory indicators (ESR and CRP). Although patients were actively treated (by NSAID, DMARD, glucocorticoid, or biologic therapy), DAS28 and other clinical parameters (SJC, TJC, CDAI) remained high, indicating poor regulation and disease progression. Compared with CTRL subjects, there were no differences between groups in terms of age (median age 65 [IQR 48–68] years for CTRL vs 65 [IQR 51–74] years for RA) or sex (male/female ratio 21/89 for CTRL vs 15/114 for RA).

To distinguish patients with RA on the basis of disease activity and to set the frame for further comparisons with laboratory and experimental data, we grouped disease parameters into variables reflecting the intensity of inflammation (CRP, ESR, TNF-α), joint destruction/bone resorption (CTX, SJC/TJC, CDAI), and autoimmune reaction (RF, ACPA), and we analyzed them with respect to DAS28 categories (Fig. 1a). Levels of both TNF-α and RF were significantly higher in severe cases (DAS28 > 5.1) than in CTRL cases and low/moderate cases (DAS28 2.6–5.1). As a marker of bone resorption rate, plasma CTX did not increase with disease severity, which can be explained by a number of factors besides RA that affect bone turnover (including patient age, glucocorticoid use, and immobility) [24]. In addition, with regard to treatment modalities, lower CTX values were observed in DMARD-treated patients (Fig. 1b).

On the basis of our previous findings [3, 12], PBMCs of CTRL subjects and subjects with arthritis were analyzed by FACS to determine the frequency of OCP populations using the gating strategy presented in Fig. 2a. Briefly, total PBMCs, depicted on forward vs side scatter plots, were analyzed by plotting lymphoid markers vs CD11b. Subsequently, a population of lymphoid-negative cells (CD3⁻CD19⁻CD56⁻) expressing CD11b (CD11b⁺) was confirmed by size and granularity to represent the monocyte subpopulation and then further dissected for the expression of CD14, CD115, and RANK. Lymphoid-negative cells that highly expressed CD11b (CD11b⁺⁺) correspond to granulocytes, whereas lymphoid-positive cells negative for CD11b correspond to lymphocytes. In addition to PBMCs, SFMCs were analyzed using a similar gating strategy to determine specific systemic-to-local differences between myeloid cell subsets.

We presumed that the inclusion of an RA cohort with a significant proportion of highly active disease (sustained inflammation and joint destruction) would allow for a clearer detection of distinct OCP subpopulations induced by arthritis. Therefore, we first determined differences in PB of CTRL and RA samples. The DP (CD11b⁺CD14⁺) subset of PB monocytes was moderately expanded in patients with RA compared with CTRL subjects (Fig. 2b). In RA, the proportion of these cells was positively associated with plasma levels of TNF-α and RF (Fig. 2c) but not with the level of CTX (not shown). The DP PB population possesses osteoclastogenic potential because we were able to obtain fully functional bone-resorbing OCs from sorted cells under stimulation by M-CSF and RANKL (Fig. 2d). However, this population generated numbers of differentiated TRAP⁺ OCs similar to those of CTRL subjects when seeded at the same densities. In line with the latter finding, the frequency of PB DP cells expressing corresponding receptors—CD115 (M-CSF receptor) and RANK (RANKL receptor)—was comparable between CTRL subjects and patients with RA, indicating similar responsiveness to the respective ligands (Fig. 2b).

We further assessed differences between the circulatory and synovial compartments of patients with RA and found that the proportion of DP monocytes was comparable between PB and SF samples (Fig. 2b). Because it was not feasible to obtain appropriate CTRL SF samples (owing to the limited amount and low cellularity of SFs in healthy synovial joints, as well as ethical unacceptability), we could not compare the SFMC profile between CTRL subjects and patients with RA. However, the proportion of DP SFMCs in RA compared with DP PBMCs in CTRL subjects was statistically higher, indicating a general increase of the DP population associated with RA. Expression of CD115 was lower in DP SFMCs than in the PBMC subset of patients with arthritis, which can be explained by enhanced shedding of membrane receptors for proinflammatory mediators seen as a negative feedback loop in inflamed tissues [25]. Nevertheless, the population of DP monocytes expressing RANK, considered to represent progenitors committed to OC lineage [3], was enlarged in the synovial vs the circulatory compartment, showing a positive correlation with TJC (Fig. 2c).

At this point, we also aimed to assess whether observed changes were specifically associated with RA compared with other forms of arthritis, as well as to analyze another group of SFMC samples (not possible to obtain from CTRL subjects). We included patients with PsA with affected peripheral joints; they resemble RA by having osteodestructive chronic inflammatory polyarthritis but are almost always seronegative with distinct pathogenic and clinical features. In general, the frequency of osteoclastogenic subpopulations followed a pattern in patients with PsA similar to that in patients with RA. Although the increase in proportion of PB DP monocytes did not reach significance, the proportion of these cells expressing RANK was significantly larger than in CTRL subjects (Additional file 1: Figure S1a). In our previous study, we indicated that the RANKL-RANK axis may be specifically associated with PsA compared with RA pathogenesis [12]. However, the level of soluble RANKL was not significantly higher in PsA SF than in RA SF (p = 0.355), possibly owing to the small sample size (Additional file 1: Figure S1b).

After a functional confirmation of osteoclastogenic potential for CD3⁻CD19⁻CD56⁻CD11b⁺CD14⁺ cells, but without increases in the number of differentiated TRAP⁺ OCs under M-CSF/RANKL stimulation in patients with arthritis, we hypothesized that the enhanced OCP activity might be caused by the complementary proinflammatory/osteoclastogenic stimuli, namely chemokine signals [3]. Monocytes, which comprise OCPs, are known to express a variety of chemokine receptors, which, upon ligand binding, initiate multiple cellular responses, including migration, proliferation, differentiation, and activation [26]. Proportions of DP subpopulations in PB were higher for several chemokine receptors in patients with RA (CCR1, CCR2, CCR4, CXCR4) (Fig. 3a) and in patients with PsA (CCR1, CXCR4) (Additional file 1: Figure S1c). In addition, receptor expression for CCR1 and CXCR4 was significantly higher in SFMCs than in PBMCs of patients with RA. Only the monocyte expression of CCR4, with unique ligands (CCL17 and CCL22) compared with CCR1 and CCR2 [14], was significantly lower in SFMCs than in PBMCs of patients with RA.

Upregulation of the distinct set of chemokine receptors on OCPs suggested their high susceptibility to chemokine signals and prompted us to determine the chemokine ligand profile in plasma and SF of patients with RA. Using the flow cytometry-based, predesigned Human Chemokine CBA Kit, we detected higher levels of CCL2, CCL3, CCL4, CCL5, CXCL9, and CXCL10 in arthritis than in CTRL plasma (Fig. 3b). Moreover, CXCL10 was significantly higher in SF than in plasma of patients with RA, indicating a chemotactic gradient for cell migration from the circulation to the synovial compartment. The increase of CCL2, CCL3 and CCL4 levels in SF of patients with RA was significant compared with CTRL plasma, considered as the basal level. However, the difference was not significant compared with RA plasma, probably owing to the small number of SF samples and high variability (with part of the measurements below the detection limit of the CBA). Additionally, CXCL12 was measured by ELISA, with no difference found between groups (Fig. 3c). Indirectly, we confirmed that PBMCs were not the major source of chemokines by analyzing their gene expression using qPCR (Additional file 2: Figure S2). With the exception of CCL5, which was abundant at protein and gene expression levels, chemokines were barely detectable by qPCR, and there were no differences between groups, suggesting that they are produced by cells other than PBMCs and egress into the circulation to exert chemotactic effects.

To this point, our data confirmed increased chemokine levels in patients with RA, which may be responsible for enhanced migration of OCPs expressing appropriate receptors. Therefore, we further assessed the association between the frequency of OCPs and their chemokine receptor expression with the level of proinflammatory mediators and clinical parameters of the disease. The proportion of PB DP monocytes was positively associated with plasma levels of CCL2 and CCL5 (Fig. 4a), and both ligands were positively associated with TNF-α (ρ = 0.549, p < 0.001 for CCL2; ρ = 0.554, p < 0.001 for CCL5). In addition, the proportion of DP subsets expressing CCR1 and CCR4 was significantly associated with plasma levels of corresponding chemokine ligands (ρ = 0.477, p = 0.006 for CCR1 with CCL3; ρ = 0.655, p < 0.001 for CCR4 with CCL2; ρ = 0.420, p = 0.010 for CCR4 with CCL3). Besides findings in patients with RA, the proportion of PB DP monocytes was positively associated with ESR in patients with PsA (ρ = 0.822, p < 0.001).

Among analyzed chemokine receptors, the DP subset expressing CXCR4 showed the most significant associations with inflammatory indicators and disease activity, including TNF-α concentration, duration of arthritis, autoantibody levels, and bone resorption (Fig. 4b). Moreover, the proportion of the DP subset expressing CXCR4 was significantly lower in patients treated with DMARD (Fig. 4c), whom we showed to also have a lower bone resorption rate (Fig. 1b). Therefore, we concluded that the highly osteoclastogenic DP population of PBMCs, especially the subset expressing CXCR4, may be used as an indicator of disease activity and response to therapy.

Finally, we assessed the contribution of analyzed data to the frequency of PB DP cells and found that multiple logistic regression was able to explain 79% of the variability of the DP subpopulation frequency (adjusted R ² = 0.790) from CCL5, CXCL10, and CXCR4 values. These variables significantly predicted the frequency of DP monocytes (p < 0.001), adding to the prediction in the following order: CCL5 (β = 0.527, p < 0.0001), CXCL10 (β = 0.522, p < 0.0001), and CXCR4 (β = 0.284, p = 0.015).

Therapeutic targeting of proinflammatory mediators has been approved for arthritis treatment to suppress inflammatory responses as well as subsequent local and systemic bone loss. Our preliminary data on PsA samples (n = 4) showed a positive association between the proportion of PB DP monocytes and DAS28, as well as reduction of both variables along with the anti-TNF treatment in two of four patients [3].

In this study, the small cohort of patients (RA, n = 4; PsA, n = 5) receiving anti-TNF therapy was followed to 6 months after the initial application. PBMCs were analyzed as previously described for patients with RA and patients with PsA (Fig. 2a). The total DP population was not suppressed by the treatment, and neither were the subsets expressing receptors for crucial osteoclastogenic factors: CD115 and RANK (Fig. 5a). In general, individual values were highly variable even at the starting point, which may be due to the fact that included patients were already in the chronic phase of the disease, having had arthritis for more than 10 years and having previously been treated with different combinations of NSAIDs, glucocorticoids, and DMARDs. However, the subpopulation of DP monocytes expressing CCR4 showed significant dynamic changes in response to anti-TNF therapy, exhibiting a significant negative trend during the anti-TNF treatment in patients with RA and patients with PsA, offering the possibility of using it as an indicator of a good therapeutic response (Fig. 5b). We observed elevated plasma levels of major CCR4 ligands (CCL2, CCL3, CCL5) and a significant positive correlation of CCR4 expression with CCL2 and CCL3 in patients with RA. Therefore, the downregulation of CCR4 may be important to reducing the susceptibility of OCPs to the chemokine gradient.

Finally, we functionally tested the effects of chemokines on circulatory monocytes in vitro for their chemotactic and osteoclastogenic properties. PBMCs from individual samples of RA patients were first stimulated by M-CSF overnight, then nonadherent cells were harvested and split into two groups: One was grown further in osteoclastogenic culture conditions under chemokine treatment to assess direct osteoclastogenic effects, and the other was plated in a Transwell culture system to determine chemotaxis of OCPs toward the chemokine gradient (Fig. 6a). The latter migration assay was further analyzed for the number of attracted cells and for their osteoclastogenic potential. For these experiments, we selected CCL2, CCL5, and CXCL10 on the basis of receptor/ligand profiling in PB samples (Fig. 3). Besides higher CCR1, CCR2, and CCR4 (that bind CCL2 and CCL5), we found that CXCR3 (receptor for CXCL10) was also increased on OCPs in patients with RA (median 43.1% [IQR 33.3–54.2] in CTRL vs 61.5% [IQR 53.3–69.2] in RA, p = 0.009). CCL2 was taken as representative of a chemokine involved only in monocyte recruitment, whereas CXCL10 was taken as a chemokine that nonselectively attracts both myeloid and lymphoid lineages. In addition, we tested CCL5, which was found to be elevated in plasma but lower in SF, indicating its role in systemic activation rather than in joint-specific homing [27]. On the basis of CXCR4 expression (Fig. 3a), CXCL12 was also selected for in vitro migration assays, but in our culture conditions, the effects on osteoclastogenesis or chemotaxis were marginal or not observed, respectively (median ratio 1.31 [IQR 1.21–1.38] for the number of OCs in CXCL12-treated vs nontreated cultures, p = 0.106; ratio 0.97 [IQR 0.77–1.56] for the number of migrated cells in CXCL12-treated vs nontreated cultures, p = 1.000).

We expected great susceptibility of PB monocytes to chemokine signals in RA, considering their higher expression of chemokine receptors than CTRL (Fig. 6b). In general, CCL2, CCL5, and CXCL10 showed measurable osteoclastogenic as well as chemotactic effects. In the direct osteoclastogenic effect, all tested chemokines showed comparable potential (doses were optimized in accordance with the median effective dose provided by the manufacturer) and generated similar numbers of TRAP⁺ OCs (Fig. 6c). In addition, the identity of differentiated OCs was further confirmed by expression of VNR and bone-resorbing activity (Additional file 3: Figure S3). Interestingly, CCL5 showed the greatest chemotactic effects among tested chemokines by attracting the highest number of cells in the migration assay (Fig. 6d). In addition, these migrated cells possessed greater osteoclastogenic potential when stimulated by RANKL and M-CSF than untreated cells and cells attracted to CCL2 and CXCL10 gradient (Fig. 6e). These results imply that chemokine signals not only stimulate chemotaxis of OCPs but also act synergistically with RANKL and M-CSF to enhance their differentiation into mature OCs.