To evaluate the pathogenic potential of K. pintolopesii, lysates of the fungus (LKP) were administered via direct injection into the paravertebral muscle tissues (Figure 1A). Consistent with its proposed pro-inflammatory role, LKP-treated mice exhibited more severe disease phenotypes, including skin ulceration, joint swelling, and spinal deformation, compared with the control group that the BALB/c ZAP70^W163C mutant mouse treated PBS (Figure 1B). Histopathological assessment of the hind limbs revealed structural disorganization in multiple articular regions: the talocrural, metatarsophalangeal, and interphalangeal joints showed blurred boundaries, with evident shrinkage or obliteration of the joint space (Figure 1C; Supplementary Figure S1). Inflammatory infiltrates and other pathological exudates were observed within the tibial cavity, and significant damage was also detected in the surrounding musculature (Figure 1C; Supplementary Figure S1). Furthermore, a marked upregulation of pro-inflammatory cytokines, including IL-1β, IL-6, and NF-κB, was detected in both spinal and joint tissues of LKP-treated ZAP70^W163C mutant mice (Figure 1C, p < 0.05). Collectively, these data demonstrate that K. pintolopesii lysate triggers autoimmune activation and robust inflammatory responses in the BALB/c ZAP70^W163C mouse model.

In order to know the way of infection of K. pintolopesii to induced arthritis, we collected the joint tissue after the two months treatments of LKP for single nuclear sequencing. After quality control we collected a total of 15751 nuclear, covering 10 major cell types (Figure 2A), including Adipocytes (207), B cells (282), Endothelial cells (1910), Fibroblasts (6561), Mesenchymal stem cells (478), Mononuclear phagocytes (4603), Myocytes (758), Osteoblasts (641), Smooth muscle cells (189) and T cells (329), and the number and UMAP distribution of each cell type were also completely different in the two groups between the control and K. pintolopesii treated (Figures 2A–D). And the expression of signature markers of each cell type showed as Gdpd2 and Csmd1 for MSCs, Pth1r and Coll1a2 for osteoblasts, Ttn, Trdn, and Neb for myocytes, Cyyr1, Ptprb, and Pecam1 for ECs, Ighd, Bank1 and Ighm for B cells (Figures 2E, F), while for the fibroblasts there was no unique gene (Figures 2E, F). RO/E (the ratio of observed over expected cell numbers) index suggested that the adipocytes (0.79), B cells (0.21), mononuclear phagocytes (1.17), myocytes (0.67), osteoblasts (0.75), and smooth muscle cells (0.63) might be involved in the process of joint injury induced by K. pintolopesii (Figure 2G, p < 0.05).

Cell-cell communication through the Interactive CellChat Explorer showed that the interaction networks increased in the K. pintolopesii treated group (Figure 2H). And the major ligands and receptors of the interaction between T cells and other 9 cell types showed in Figure 2I, as Sema4d-Plxnb1/Plxnb2/Plxnd1, Ntn1-Unc5b, Lgals3-Mertk, Integrin a4b1-complex Plaur for the ligands of T cells communicated to the receptors of osteoblasts (Figure 2I), which indicated that the T cells interact with the receptor protein molecules from the targeted osteoblasts by secreting donor protein molecules or ligands. The top 40 differentially expressed genes showed in Supplementary Figure S2A, and the KEGG analysis showed that the pathway of Hif-1 signaling (marker genes of Ctla4, Hif1a, Serpine1, Timp1), Rheumatoid arthritis (Ctla4, Mmp3, Tcirg1), ECM-receptor interaction (Itga5, Lamb1), IL-17 signaling (Mmp3, S100a8, S100a9) were influenced (Figure 3A), which suggested that K. pintolopesii activates these pathways in the T cells of ZAP70^W163C mutant mice.

In addition, the lysate of K. pintolopesii were added into the primary spleen T cells from ZAP70^W163C mutant mice (LKP-TCM). The RNA-sequence of LKP-TCM showed that the inflammatory markers of Cxcl1, IL-22, IL-1β, Saa3, IL-6, Cxcl2, Ccl5, Ccl6, Il-4i1, IL-1a, TNF, and IL-17F were significantly up-regulated (Figures 3B, D, F, p < 0.05); and the KEGG analysis of up-regulated genes were enriched into TNF signaling pathway, Cytokine-cytokine receptor interaction, IL-17 signaling pathway and Inflammatory bowel disease (Figure 3E, p < 0.05), and these changes may be driven by the genes of Tcf7, Foxp3, Batf, Gata3 and Ikzf2 (Supplementary Figure S2H), the inflammatory reaction was not so severely while the mitochondrial genes were changed in the LKP-TCM of wildtype mice (Figure 3G, p < 0.05). All which indicated that the K. pintolopesii also acts directly on T cells, then induced severe inflammatory reaction on ZAP70^W163C mutant mice, also induced mitochondrial dysfunction in the normal individual.

The changes of other cell type of B cells, adipocytes and myocytes showed in Figures 3E, F and Supplementary Figures S2B–G, the differentially expressed genes in all the three cell types were enriched in the IL-17 signaling pathway (Figures 3E, F; Supplementary Figures S2B–G). And these changes might be driven by the genes of Dmrta2, Pou2af1, Bach2, Zfp831 and Irx5 for B cells; Srebf1, Trp63, Thrb, Nr1h3 and Isl1 for adipocytes; Zfp612, Pgam2, Esrrg, Myod1 and Pitx2 for myocytes (Supplementary Figure S2H). While all the above bioinformatics analysis results need to be verified by test experiments.

Traditional classifications (e.g., M1/M2 polarization) often oversimplify macrophage heterogeneity by ignoring dynamic functional states, developmental trajectories, and tissue-specific adaptations. To further investigate the functional plasticity, developmental continuity, tissue-specific niches of K. pintolopesii on mononuclear phagocytes in BALB/c ZAP70^W163C mutant mouse, we performed a new in-depth subpopulation analysis based on preliminary single-nucleus RNA sequencing data. Although initial RO/E index analysis suggested that mononuclear phagocytes were not broadly affected by K. pintolopesii (Figure 2), further refinement of cell clustering identified 11 distinct subpopulations (Figure 4A): conventional dendritic cells (421), Mac_Adam8 (287), Mac_Hmox1 (451), Mac_Kcnip4 (191), Mac_Retnla (454), Mac_Spic (Hanzelmann et al., 2013), Mac_Vsig4 (319), Mac_mt-Co1 (615), ProMac_Mki67 (161), monocytes (307), and osteoclasts (Hanzelmann et al., 2013). Notably, macrophages were subdivided into eight functionally distinct subsets. Uniform Manifold Approximation and Projection (UMAP) revealed substantially altered distribution patterns in the K. pintolopesii treated group compared to controls (Figures 4B, C). The relative abundances of most subpopulations were significantly modified following fungal exposure (Figure 4D, p < 0.05). Signature markers for each macrophage subset were identified: Fos and F7 for Mac_Adam8; Stab1 and Hal for Mac_Hmox1; Vsig4 and Arsb for Mac_Kcnip4; Selenop and Plekhg5 for Mac_Retnla; Vcam1 and Abcg3 for Mac_Spic and Mac_Vsig4; Itga2b and Top2a for ProMac_Mki67; and mt-Atp6, mt-Cytb, and mt-Nd1 for Mac_mt-Co1 (Figures 4E, F).

Pseudotemporal trajectory analysis demonstrated a profound reorganization of cellular states. Cells located in the lower left quadrant in controls either transdifferentiated into other subtypes or were entirely absent in the treated group, indicating a comprehensive reshuffling of macrophage subpopulations induced by K. pintolopesii (Figure 4G). Differential gene expression analysis between treated and control groups highlighted significant downregulation of mitochondrial genes (Figure 4H, see also in Figure 5A). KEGG pathway analysis indicated enrichment in osteoclast differentiation, cytokine–cytokine receptor interaction, IL-17 signaling and PI3K–AKT signaling, suggesting that K. pintolopesii triggers inflammatory responses and mitochondrial oxidative stress in murine hosts (Figure 4I). RO/E indices further indicated a positive association of Mac_Adam8 (1.59) and Mac_mt-Co1 (1.64) with arthritis and spondylitis, whereas Mac_Kcnip4 (0.38), Mac_Retnla (0.08), Mac_Spic (0.64), and Mac_Vsig4 (0.52) were negatively correlated (Figure 5B, p < 0.05). Cell–cell interaction networks revealed enhanced cross-talk among macrophage subgroups in the treated group (Supplementary Figures S3A, B). Subpopulation specific KEGG analysis indicated that Mac_Adam8 exhibited activation of ferroptosis, TNF signaling, apoptosis, NOD-like receptor signaling, and lysosomal pathways, along with Salmonella and Legionella infection pathways, but inhibition of osteoclast differentiation and endocytosis (Figure 5C, p < 0.05). In Mac_mt-Co1, IL-17 signaling, ferroptosis, TNF signaling, rheumatoid arthritis, and PI3K–AKT pathways were significantly activated (Figure 5D, p < 0.05). Mac_Retnla showed enrichment in endocytosis, MAPK signaling, and parathyroid hormone pathways (Figure 5E, p < 0.05), while Mac_Kcnip4 was enriched in lysosome and phagosome pathways (Figure 5F, p < 0.05), implying impaired phagocytic function in these subsets upon fungal challenge.

Notably, cholesterol metabolism was upregulated in both Mac_Adam8 and Mac_mt-Co1 (Figures 5C, D, p < 0.05). Given that aberrant macrophage cholesterol metabolism is known to contribute to rheumatoid arthritis pathogenesis via dysregulated uptake, transport, esterification, and storage, these changes suggest a role in promoting inflammation and bone destruction (Tejera-Segura et al., 2017; Quevedo-Abeledo et al., 2021). Candidate driver genes for Mac_Adam8 included Cebpb, Nuak2, Rest, Nfkb2, and Gabpa (Figure 5G, p < 0.05), while Trp63, Gm35315, Six1, Hoxa2, and Zfp3were identified as potential therapeutic targets. For Mac_mt-Co1, central regulators included Prdm15, Klf16, Cebpb, Gm29609, and Rxra (Figure 5H, p < 0.05), with Bnc2, Plagl1, Sp7, Churc1, and Gm14399representing possible targets for intervention (Supplementary Figure S3C, p < 0.05).

To validate these findings, bone marrow-derived macrophages from BALB/c ZAP70^W163C mutant mice (BMDM-ZAP70) were treated with K. pintolopesii lysate (LKP). RNA sequencing identified 2,622 differentially expressed mRNAs (Figure 5I, p < 0.05; Supplementary Figure S4A, p < 0.05). Proinflammatory cytokines (IL-1β, IL-6, IL-7, IL-18, TNF-α) were markedly upregulated (Supplementary Figures S4A–D, p < 0.05), along with PANoptosome components (ZBP1, RIPK3, RIPK1, caspase-8, caspase-6, ASC, NLRP3) and interferon-related genes (Supplementary Figures S4A, B, p < 0.05). KEGG analysis of upregulated genes indicated activation of HIF-1, NF-κB, and TNF signaling pathways (Figure 5J, p < 0.05), suggesting integrated dysregulation of oxygen homeostasis, immune activation, and energy metabolism that sustains chronic inflammation and tissue remodeling. Enrichment of pathways related to Epstein-Barr virus, human T-cell leukemia virus 1, and Kaposi sarcoma-associated herpesvirus infections implied broad compromise of immune defense mechanisms (Figure 5J, p < 0.05). Conversely, downregulated genes were enriched in pathways including herpes simplex virus 1 infection, NOD-like receptor signaling, and apoptosis (Supplementary Figure S3D, p < 0.05). Collectively, these results demonstrate that LKP induces severe inflammatory responses in BMDM-ZAP70 macrophages.

Further analysis revealed that osteoblasts could be subdivided into three distinct subpopulations (Figure 6A). Both UMAP visualization and cell proportion analyses demonstrated significant alterations following K. pintolopesii treatments (Figures 6B–D, p < 0.05). In the osteoblasts_Slc13a5 subpopulation, signature marker genes including Cmss1, mt-Co1, mt-Co3, mt-Cytb, mt-Atp6, Slc39a14, mt-Co2, Col11a2, Tcirg1, Cdk11b, and Col1a1were significantly downregulated (Figure 6E, p < 0.05), while transcription factors such as Junb, Bdp1, Ppard, Irf2, Rreb1, and Klf3 were upregulated (Figure 6H, p < 0.05).

The RO/E index indicated that the osteoblasts_Gapd2 subpopulation (0.70) was negatively associated with arthritis and spondylitis, whereas osteoblasts_Slc13a5 (1.91) showed a positive correlation (Figure 6F, p < 0.05). KEGG enrichment analysis of differentially expressed genes highlighted several key pathways, including ECM-receptor interaction (involving Tnn, Tnc, Spp1, Itga5, Col1a1, and Col1a2), protein digestion and absorption (Col11a2, Col1a1, Col1a2, Col5a1, and Col5a3), and the PI3K-Akt signaling pathway (Col1a1, Col1a2, Tnn, Tnc, Spp1, and Itga5) (Figures 6G, H, p < 0.05). In the osteoblasts_Gapd2 subpopulation, pathways such as proteoglycans in cancer (Col1a1, Col1a2, Hcls1, Hif1a, Stat3, Vav1), IL-17 signaling (Mmp13, Mmp3, S100a9, Ccl7, Cebpb), and ECM–receptor interaction (Col11a2, Col1a1, Ibsp, and Tnn) were activated (Figure 6I, p < 0.05). Transcription factors including Junb, Bdp1, Lyl1, Tagln2, Tcfl5, Tbx2, and Foxn3may contribute to these changes (Figure 6J, p < 0.05). Additionally, fermentation products of Enterococcus faecium not only alleviated but also reversed pathological symptoms—including alopecia and joint swelling—with statistical significance (Figures 6K, L, p < 0.05). Further details regarding the therapeutic efficacy and underlying mechanisms of E. faecium will be elucidated in our subsequent manuscript.

Following treatment with K. pintolopesii, fibroblast-like synoviocytes (FLS) were subdivided into six distinct subpopulations (Figure 7A), Fib_Acan (166), Fib_Angptl7 (910), Fib_Celf2 (1863), Fib_Cmss1 (2051), Fib_Col22a1 (712), and Fib_Tnc (859). UMAP analysis revealed marked alterations in the spatial distribution of these subpopulations in the treated group (Figures 7B, C, p < 0.05). Signature marker expression for each subpopulation is detailed in Figures 7D, E. Pseudotemporal trajectory analysis indicated a substantial disruption in the developmental path of FLS following fungal exposure (Figure 7F, p < 0.05). Differential gene expression analysis identified 1695 upregulated and 1060 downregulated genes in the K. pintolopesii treated group (Figure 7G, p < 0.05). Notably, the most significantly downregulated genes were mitochondrial, including mt-Co1, mt-Co3, mt-Atp6, mt-Cytb, mt-Nd1, and mt-Nd4 (Figure 7G, p < 0.05). Pathway enrichment analysis revealed activation of FoxO signaling, axon guidance, Rap1 signaling, focal adhesion, and TGF-β signaling pathways (Figure 7H, p < 0.05).

The RO/E index indicated that Fib_Acan (0.09), Fib_Angptl7 (0.28), and Fib_Celf2 (0.16) were negatively correlated with arthritis and spondylitis, whereas Fib_Cmss1 (1.66) and Fib_Tnc (2.15) showed positive correlations (Figure 7I, p < 0.05). The relative abundance of most subpopulations was significantly altered post-treatment (Figure 8A, p < 0.05). In particular, a substantial proportion of cells differentiated into the Fib_Cmss1 (marked by Cmss1, Mmp3, and mt-Co3; Figures 7D, E) and Fib_Tnc (marked by Tnc, Cxcl5, and Gm6093; Figures 7D, E) subsets, suggesting that these two subpopulations may contribute to the loss of clear tissue and organ boundaries observed in Figures 1C and 6J, K.

Further pathway analysis in the Fib_Tnc subpopulation showed significant enrichment in Ribosome biogenesis in eukaryotes (key genes: Acin1, Ddx39b, Rbm25, Stf3b1; Supplementary Figure S5A), Spliceosome (Acin1, Ddx39b, Rbm25, Stf3b1), IL-17 signaling (Cxcl5, IL-17RA, IL-1b, IL-1R1, IL-6, Mmp13, S100a9), Nucleocytoplasmic transport (Acin1, Ddx39b, IL-6, Nv1), Osteoclast differentiation (Ncf2, Fosl1/2, IL-1b, Snrnp70), Lysine degradation (Cebpb, Ipo5, Sart1, Sf3b3) (Figure 8B, p < 0.05). Additionally, the Fib_Cmss1 subpopulation exhibited activation of focal adhesion, TNF signaling, IL-17 signaling, and PI3K-AKT signaling pathways (Figure 8C, p < 0.05; Supplementary Figure S5B). The Fib_Celf2 subpopulation showed enrichment in Rap1 signaling, TGF-β signaling, MAPK signaling, and PI3K-AKT pathways (Figure 8D, p < 0.05; Supplementary Figure S5C). Fib_Acan was associated with glucagon signaling, protein digestion and absorption, HIF-1 signaling, and cGMP-PKG signaling pathways (Figure 8E, p < 0.05; Supplementary Figure S5D), while Fib_Angptl7 demonstrated activation of ECM-receptor interaction, IL-17 signaling, and focal adhesion pathways (Figure 8F, p < 0.05; Supplementary Figure S5E). These findings collectively indicate an expansion of pro-inflammatory fibroblast subpopulations in K. pintolopesii treated mice. RSS (Regulon specificity score) analysis identified potential driver genes for each subpopulation, Larp1, Tead1, Fosl1, Arid3a, and Bcl3 for Fib_Tnc; Larp1, Mlx, Arid3a, Fosl1, and Prdm4 for Fib_Cmss1; Ar, Tcf7l1, Twist, Nfib, and Lmx1a for Fib_Celf2; and Sox8, Hoxa5, Lef1, Foxa2, and Kdm4d for Fib_Acan (Figure 8G, p < 0.05).

In a complementary experiment, primary spleen T cells from ZAP70^W163C mutant mice were treated with K. pintolopesii lysate (LKP), and the resulting conditioned medium was applied to muscle stem cells (MuSCs) in proliferation medium. This treatment induced rapid proliferation and fibrosis of MuSCs (Figure 8H). However, when the same conditioned medium was applied to differentiation medium, no obvious fibrotic or differentiation effects were observed (data not shown). These results suggest that K. pintolopesii may initiate fibrosis in muscle and/or bone tissues by activating stem cells (such as muscle stem cells or periosteal stem cells), though further validation is required to substantiate this mechanism.

In rheumatoid arthritis (RA), endothelial cells (ECs) are now recognized as active contributors to disease pathogenesis rather than passive targets. They promote disease progression by regulating central inflammatory processes such as leukocyte extravasation, angiogenesis, cytokine release, and protease production (Nygaard and Firestein, 2020; Wei et al., 2020; Zhao et al., 2023). Endothelial dysfunction compromises vascular integrity, facilitates leukocyte infiltration, and exacerbates synovial hypoxia, thereby accelerating synovial hyperplasia and joint damage (Patidar et al., 2022; Ahmed et al., 2023).

In this study, we further dissected the heterogeneity of synovial ECs and identified six distinct subpopulations (Figure 9A), AECs_Mgp (267), CapECs_Stab2 (255), LECs_Ccl21a (486), VECs_Col15a1 (326), VECs_Mcam (196), and VECs_Vwf (380). UMAP revealed pronounced compositional shifts in the K. pintolopesii treated group relative to controls (Figures 9B, C), with significant alterations in the abundance of most subpopulations (Figure 9D, p < 0.05). Key marker genes for each subset were identified, CapECs_Stab2: Stab2, Abcc9, Tfpi; AECs_Mgp: Mgp, Sema3g, Atp2a3; LECs_Ccl21a: Ccl21a, Mmmrn1, Reln; VECs_Col15a1: Col15a1, Rasal1, Insr; VECs_Mcam: Col15a1, Mcam, mt-Co3; VECs_Vwf: Vwf, Lepr, Lifr (Figures 9E, F). Pseudotemporal trajectory analysis demonstrated a substantial reorganization of cellular states. Clusters corresponding to states 1–3 in controls were entirely absent or transformed into other subtypes following K. pintolopesii exposure (Figures 9G, H), indicating profound disruption of endothelial subpopulation architecture.

Notably, mitochondrial genes (e.g., mt-Co3, mt-Co2, mt-Co1, mt-Atp6, Cmss1, mt-Nd1, mt-Nd4, mt-Nd3, mt-Nd2, mt-Cytb) were significantly downregulated in the treated group, whereas genes including Gsn, Fhl1, Cst3, Htra4, Neb, Sparcl1, Igfbp5, Cavin1, and Cavin2were upregulated (Figure 9I, p < 0.05). KEGG pathway analysis suggested that these changes reflect altered mitochondrial function, impaired migratory capacity, dysregulated stress responses, and modifications in IGF signaling, cell adhesion, and proliferation-related pathways (Figure 10A), and Figure 10B details expression trends of marker genes across all six subpopulations.

The RO/E index indicated that VECs_Mcam (1.78) and VECs_Col15a1 (1.38) were positively correlated with arthritis and spondylitis, whereas AECs_Mgp (0.43) and VECs_Vwf (0.50) were negatively correlated (Figure 10C, p < 0.05). In VECs_Mcam and VECs_Col15a1 subpopulations that expanded significantly after treatment (Figures 9B–D), KEGG analysis revealed activation of focal adhesion, ECM-receptor interaction, and PI3K-Akt signaling pathways (Figures 10D, E, p < 0.05). Additionally, VECs_Mcam exhibited enrichment in rheumatoid arthritis, IL-17 signaling, and TNF signaling pathways (Figure 10E, p < 0.05; see also Supplementary Figure S6), suggesting that VECs_Col15a1 may be involved in stress sensing and transduction, while VECs_Mcam likely contributes to inflammatory initiation. Conversely, AECs_Mgp and VECs_Vwf showed activation of cytokine-cytokine receptor interaction, ECM-receptor interaction, and IL-17 signaling (Figures 10F, G, p < 0.05; Supplementary Figure S6), implying that these subpopulations may respond to cytokine signals from VECs_Mcam and VECs_Col15a1, thereby amplifying stress and inflammatory responses.

Using Modified Martin’s Medium containing peptone 5.0g, Yeast extract powder 2.0g, glucose 20.0g, dipotassium hydrogen phosphate 1.0g, and magnesium sulfate 0.5g. The culture was incubated at 28°C on a shaker for 5 days, followed by centrifugation at 2000 rpm for 15 minutes, then collected the mycelium.

Mycelium is harvested, washed with cold PBS twice, resuspend in cold PBS. Mechanical grinding of the cell wall using a non-contact fully automatic ultrasonic cell disruption instrument (Bioruptor PICO). Protein concentration is determined via BCA or Bradford assay, and aliquots are stored at -80°C to prevent degradation. Key precautions include maintaining low temperatures, avoiding repeated freeze-thaw cycles, and optimizing buffer composition for downstream applications (e.g., avoiding strong detergents for enzyme activity assays).

Using Modified Martin’s Medium containing peptone 5.0g, Yeast extract powder 2.0g, glucose 20.0g, dipotassium hydrogen phosphate 1.0g, and magnesium sulfate 0.5g. The culture was incubated at 28°C on a shaker for 7 days, followed by centrifugation at 2000 rpm for 15 minutes, then collected liquid supernatant.

Protein concentration is determined via BCA or Bradford assay, and aliquots are stored at -80°C to prevent degradation. Key precautions include maintaining low temperatures, avoiding repeated freeze-thaw cycles, and optimizing buffer composition for downstream applications (e.g., avoiding strong detergents for enzyme activity assays).

All mouse procedures were approved by the Institutional Animal Care and Use committee of the Bo-Jin Biotechnology Co. LTD (Animal Ethics Application NO. BG-SMP-001V1-001). BALB/c ZAP70^W163C mutant mouse were purchased from the Changzhou Kalai Biotechnology Co. LTD [SCXK (Su) 2021-0013], then feeding and breeding in the Bo-Jin Biotechnology Co. LTD [SCXK (Yue) 2020-0051], and pair-housed in plastic cages in a temperature- controlled (25°C ± 2°C) colony room with a 12/12-h light/dark cycle. Food and water were available ad libitum. All efforts were made to minimize the number of animals used.

The basic culture protocol is as follows (1), Typically use 6-8-week-old BALB/c ZAP70^W163C mutant mice (2). High-glucose DMEM, supplemented with 10% FBS (Fetal Bovine Serum) and 1% Penicillin-Streptomycin (double antibiotics), added M-CSF (macrophage colony-stimulating factor) at 15% (v/v) (3). Bone marrow cell extraction, aseptically remove the femur and tibia from mice; thoroughly disinfect the bone surfaces by soaking in 75% ethanol (approximately 5 minutes, multiple times), then rinse with pre-cooled PBS to remove ethanol; cut off both ends of the bones. Flush the bone marrow cavity using a syringe (1–2 mL) filled with pre-cooled base medium to wash cells into a culture dish; repeatedly pipette the cell suspension to dissociate cell clumps, and filter through a 200-μm cell strainer to obtain a single-cell suspension; centrifuge (e.g., 1200 rpm, 5 minutes) to collect cells (4). Macrophage induction and differentiation, resuspend cells in complete DMEM containing 20–50 ng/mL M-CSF; seed the cells in culture dishes (bone marrow cells from one mouse can be seeded into 4-6 × 10 cm dishes); culture in a 37°C, 5% CO₂ incubator, typically, add an equal volume of fresh induction medium on day 2.5 of culture; by approximately day 3, a large number of adherent macrophages (often with “small tail” morphology) can be observed, and cells are ready for experimentation on day 5 or 6.

The isolation and culture of muscle stem cells (MuSCs) using the differential adhesion method involves digesting muscle tissue with enzymes like collagenase I/neutral protease II and trypsin substitute to create a single-cell suspension. This suspension is then subjected to sequential plating (e.g., PP1 to PP6), where faster-attaching cells like fibroblasts adhere first (within hours), allowing the slower-attaching MuSCs to be enriched from the supernatant and collected for further culture. The purified MuSCs are typically cultured in a serum-free medium supplemented with factors like recombinant epidermal growth factor and glutamine to promote proliferation and maintain stemness, often on surfaces coated with type I collagen to facilitate attachment. This approach effectively isolates Pax7-positive MuSCs capable of self-renewal and differentiation.

Animal joint and spinal tissues were dissected. A total of four Joint and spinal tissues from each group were fixed in 4% paraformaldehyde solution and prepared as paraffin sections. Sections were stained with hematoxylin-eosin (H&E), and immunostained for IL-6 (Servicebio, GB11117, 1:600), IL-1β (Servicebio, GB11113, 1:1000), NF-κB (Servicebio, GB11997, 1:100), using paraffin-embedded 3 μm sections and a two-step peroxidase conjugated polymer technique (Servicebio, G1004, G1001, G1040, G1212, and G1202, Wuhan China; Beyotime, C0265, China). Slides were observed by light microscopy.

To obtain a high-resolution map of Mononuclear phagocytes, cells from the specific cluster were extracted. Scanpy v1.9.3 was used for quality control, dimensionality reduction and clustering under Python 3.10. Top 2000 variable genes were selected by setting flavor = ‘seurat_v3’. Principle Component Analysis (PCA) was performed on the scaled variable gene matrix, and top 15 principle components were used for clustering and dimensional reduction. Cells were separated into 15 clusters by using Louvain algorithm and setting resolution parameter at 1.2. Cell clusters were visualized by using Uniform Manifold Approximation and Projection (UMAP). To annotate the cell clusters, DEGs were identified by the scanpy.tl.rank_genes_groups() function based on Wilcoxon rank sum test with default parameters. The cell groups were annotated based on the DEGs and the well-known cellular markers from the literature.

NA concentration and purity were determined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). RNA integrity was evaluated with the RNA Nano 6000 Assay Kit on an Agilent Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA), which provides an RNA Integrity Number (RIN) ranging from 1 to 10 to quantitatively assess sample quality.

A total of 1 μg RNA per sample was used as input for library preparation. Sequencing libraries were constructed using the NEBNext Ultra™ RNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA, USA) in accordance with the manufacturer’s instructions. Unique index codes were incorporated to assign sequences to respective samples. Briefly, mRNA was enriched from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was performed in the presence of divalent cations at elevated temperature using NEBNext First Strand Synthesis Reaction Buffer (5X). First-strand cDNA was synthesized with random hexamer primers and M-MuLV Reverse Transcriptase. Second-strand cDNA was subsequently generated using DNA Polymerase I and RNase H. Remaining overhangs were converted to blunt ends through exonuclease/polymerase activity. Following adenylation of the 3′ ends, NEBNext adaptors with hairpin loop structures were ligated to the cDNA fragments.

Library fragments were purified using the AMPure XP system (Beckman Coulter, Beverly, MA, USA) to select products of approximately 240 bp in length. The size-selected, adaptor-ligated cDNA was treated with 3 μL of USER Enzyme (NEB) at 37°C for 15 min, followed by incubation at 95°C for 5 min. PCR amplification was performed using Phusion High-Fidelity DNA Polymerase with universal PCR primers and index (X) primers. The PCR products were purified with the AMPure XP system, and library quality was validated on the Agilent Bioanalyzer 2100 system.

Cluster generation was conducted on a cBot Cluster Generation System using the TruSeq PE Cluster Kit v4-cBot-HS (Illumina). Finally, the libraries were sequenced on an Illumina platform to generate paired-end reads.

Prism 8 and SPSS 16.0 software were used for statistical analyses. Significances between two groups were analyzed using two-tailed unpaired Student’s t-tests or Mann–Whitney U-tests. The significance of the growth curves was analyzed by two-way ANOVA. Survival curves were determined using the Kaplan–Meier method with the log-rank test. The data are presented as the mean ± SD except where stated otherwise. The differences with *p < 0.05, **p < 0.01, or ***p < 0.001 were considered statistically significant.