Revised: August 20, 2026
Accepted: October 30, 2026
Published online: November 18, 2026
Processing time: 127 Days and 12.6 Hours
Facet joint osteoarthritis (FJOA) is a chronic degenerative joint disease, which can lead to severe low back pain. However, the pathophysiological mechanisms un
To investigate the cellular heterogeneity, potential therapeutic markers, and un
We determined the cell landscapes of human LFJ cartilage using single-cell RNA sequencing.
The proportions of proliferative chondrocytes and proinflammatory chondrocytes were significantly increased in mild degenerated LFJ samples, while those of fibrocartilage chondrocytes, effector chondrocytes, and homeostatic chondrocytes were increased in severe degenerated LFJs. Importantly, proinflammatory chondrocytes were found in mild degenerated LFJs, but not in severe degenerated LFJs. We also found that severe degenerated LFJs exhi
Our findings deepen the understanding and extend distinguishing features of cellular heterogeneity and inter
Core Tip: This study provides a single-cell transcriptomic landscape of human lumbar facet joint (LFJ) cartilage degeneration across different degeneration stages. We identified stage-specific changes in chondrocyte and macrophage subpopulations and characterized their potential interactions during disease progression. Notably, proinflammatory chondrocytes were enriched in mild degenerated LFJs, whereas distinct macrophage populations emerged with advanced degeneration. Cell-cell communication analysis further revealed enhanced chondrocyte-macrophage crosstalk during degeneration. These findings provide new insights into the cellular heterogeneity and potential intercellular mechanisms underlying LFJ degeneration.
- Citation: Chen XY, Luo W, Chen YZ, Wang L, Zhang YY, Li HY, Wang P, Man ZT, Li W, Wang DD. Single-cell RNA-sequencing analysis reveals cellular states and intercellular communication of human lumbar facet-joint degeneration. World J Orthop 2026; 17(11): 125372
- URL: https://www.wjgnet.com/2218-5836/full/v17/i11/125372.htm
- DOI: https://dx.doi.org/10.5312/wjo.125372
Lumbar facet joint osteoarthritis (FJOA) is a prevalent degenerative disease of the spine, accounting for 15%-41% of patients with chronic low back pain (LBP)[1,2]. FJOA can lead to a series of LBP symptoms and neurological problems[3]. People of various ages who experience LBP may suffer mobility challenges and reduced quality of life, which in turn have enormous impacts on healthcare systems and economic development[4,5].
The lumbar facet joints (LFJs) are synovial joints in the posterior aspect of the spine that are covered with hyaline cartilage and enclosed by synovium and a joint capsule[6]. LFJ degeneration is considered the leading cause of FJOA and facetogenic chronic LBP[7,8]. It has been reported that degeneration of the facet joints can occur as early as 15 years of age[7]. Like appendicular joints, LFJ exhibits a similar pathological process of degenerative changes involving cartilage degeneration, subchondral bone erosion, joint-space narrowing, osteophyte formation, and synovial inflammation[4]. Many recent studies have used single-cell RNA sequencing (scRNA-seq) to explore articular chondrocytes, subchondral bone, synovium, and immune cells, revealing the heterogeneity and pathologic mechanisms of human osteoarthritis[9-12].
Previous molecular studies specific to LFJ degeneration have identified several key pathogenic pathways. For instance, transcriptomic analyses have suggested the aberrant activation of the Wnt and nuclear factor kappa B signaling pathways in FJOA[13,14]. Furthermore, cellular senescence and the upregulation of associated genes have been implicated in the progression of FJOA degeneration[15]. While these studies provide crucial molecular insights, they often rely on bulk tissue analysis, which masks cellular heterogeneity. Concurrently, scRNA-seq has been revolutionary in exploring appen
Given the complexity and poor understanding of human FJOA, we used scRNA-seq to identify cell populations and to explore the heterogeneous landscapes and mechanisms of intercellular crosstalk that occur during the degeneration of LFJ. Our results allowed us to uncover the endogenous heterogeneity and novel phenotypes of chondrocytes, and to elucidate the pathophysiological changes and mechanisms of human LFJ degeneration and FJOA progression. These findings provide potential avenues for future diagnostic and therapeutic investigations for human FJOA and facetogenic spine disorders.
Six patients with lumbar degenerative diseases who underwent lumbar posterior surgery were recruited from the 960th Hospital of The People’s Liberation Army Joint Logistics Support Force. The LFJ degeneration grades of all six parti
Under sterile conditions, the freshly collected cartilaginous LFJ tissues were washed twice with pre-cooled RPMI 1640 medium containing 0.04% bovine serum albumin (BSA). The tissues were then mechanically dissociated into approximately 0.5 mm3 fragments using surgical scissors and transferred into freshly prepared enzymatic digestion solution. The digestion mixture contained RPMI 1640 (Corning, cat. No. 10-040-CVR), 0.04% BSA (MACS, cat. No. 1000076), and 0.2% collagenase II (Gibco, cat. No. 17101015). The samples were incubated at 37 °C for 30-60 minutes with gentle inversion every 5-10 minutes. The digested cell suspension was filtered through a BD 40 μm cell strainer (Falcon, cat. No. 352340) 1-2 times. The filtrate was centrifuged at 300 ×g for 5 minutes at 4 °C. The cell pellet was resuspended in appropriate medium, mixed with an equal volume of red blood cell lysis buffer (Miltenyi, cat. No. 130-094-183), and incubated at 4 °C for 10 minutes. After centrifugation at 300 ×g for 5 minutes, the supernatant was discarded. The pellet was washed once with medium followed by another centrifugation at 300 ×g for 5 minutes, and the final supernatant was removed. Finally, the cells were re-suspended with 1 mL RPMI 1640 medium (Corning, cat. No. 10-040-CVR) with 0.04% BSA added. Single-cell suspension concentration and cell viability were then evaluated by Luna-FL cell counter (Logos Biosystems, Korea) or Trypan Blue staining method.
Cartilaginous tissue sections used for immunohistochemistry were also obtained from two other patients who required lumbar posterior surgery for lumbar degenerative diseases. The detailed clinical characteristics of these two patients are also listed in Supplementary Table 1.
The scRNA-seq libraries of human LFJ cartilage were prepared using the Chromium Next GEM Single Cell 3ʹ Reagent Kit v3.1 (10× Genomics, America), in accordance with the manufacturer’s protocols. Briefly, the cells were isolated and resuspended in Master Mix from the Reagent Kit at a concentration of 700-1200 cells/µL (viability ≥ 85%). The gel beads, Cell Mix, and partitioning oil were loaded onto the Single Cell Chip G. The GEMs microdroplet was immediately performed on the 10× Chromium microfluidic platform. A sufficient mass of barcoded, full-length complementary DNA was amplified by PCR to construct libraries. The amplified complementary DNA was fragmented and end-repaired, and double-sided size selection was conducted using SPRIselect reagents. After quality control, the libraries were sequenced on an Illumina Nova 6000 PE150 platform to generate 150-bp paired-end reads (OE Biotech Co. Ltd, Shanghai, China).
The Cell Ranger software pipeline (version 5.0.0) provided by 10× Genomics was used for alignment and quantification of the raw scRNA-seq data from each sample. We processed the unique molecular identifier (UMI) count matrix using the R package Seurat (version 4.0.0)[22]. For quality control, cells with the following criteria were included for downstream analyses: (1) The nCount_RNA ≥ 1000; (2) The nFeature_RNA ≥ 200; (3) The log10GenesPerUMI > 0.7; and (4) The mito
Macrophages were extracted from the integrated single-cell dataset and subjected to secondary unsupervised clustering based on their transcriptomic profiles. The resulting subclusters were identified according to cluster-specific DEGs and canonical macrophage markers. Subcluster annotation was further supported by the expression of characteristic marker genes and functional enrichment analysis.
Cell-cell communication networks among chondrocytes and macrophages were inferred using the CellChat R package (version 2.1.2) based on ligand-receptor interactions. First, we imported the normalized expression matrix to create the cellchat object with the create CellChat function. Secondly, the data was preprocessed with the identifyOverExpres
Monocle2 (version 2) was used to determine the cell lineage of subclusters[28]; specifically, reduced-dimension clustering was performed using the reduceDimension function, while the orderCells function was used to infer the cell lineage differentiation trajectory. The spliced reads and unspliced reads for each gene in every cell were re-counted by velocy.py based on the data output from Cell Ranger[29]. The velocity of each cell for every gene was calculated using the velo
The cellular communications between cell subclusters were further analyzed by degeneration grade. The CellPhoneDB[30] (version 4.1.0) was used to identify biologically relevant ligand-receptor interactions from single-cell transcriptomics (scRNA-seq) data. A ligand or a receptor was defined as “expressed” in a particular cell type if 10% of the cells of that type had non-zero read counts for the ligand/receptor encoding gene. Statistical significance was then assessed by randomly shuffling the cluster labels of all cells and repeating the above steps, which generated a null distribution for each LR pair in each pairwise comparison between two cell types. After running 1000 permutations, P values were calculated with the normal distribution curve generated from the permuted LR pair interaction scores. To define net
Place the tissue in a decalcifying reagent (Servicebio, Wuhan, Hubei Province, China) and replace the fresh solution daily until the tissue becomes soft. Rinse with tap water for 24 hours. Dehydrate the tissue using a gradient alcohol solution. After the tissue block is dehydrated with alcohol, it needs to be cleared. Add transparent reagent (Servicebio, Wuhan, Hubei Province, China) to clear the tissue. The cleared tissue block is then immersed in 3 cylinders of paraffin (60 °C) for wax impregnation. The wax-impregnated tissue block is wrapped around the paraffin block. The temperature of the embedding wax should be slightly higher than the wax impregnation temperature to ensure that the tissue block is completely integrated with the embedding paraffin.
Cartilage tissue sections were deparaffinized with dimethylbenzene and sequentially rehydrated with 100%, 95%, 90%, 80%, and 70% ethanol. After washing with phosphate-buffered saline (PBS), the sections were boiled in buffer containing 0.01 M sodium citrate-hydrochloric acid (pH 6.0) at 98 °C for 20 minutes for antigen retrieval. After three washes with PBS, the sections were rinsed in 3% peroxidase quenching solution (Invitrogen, America). Then 3% bovine serum albumin (Servicebio, Wuhan, Hubei Province, China) was used to block endogenous peroxidase. The sections were then incubated with primary antibody (Supplementary Table 3) at 4 °C overnight. After three washes with PBS, the sections were incu
The Wilcoxon test was used to analyze differences in the percentages of subclusters between mildly and severely de
To fully characterize the cellular heterogeneity in human LFJ, we profiled LFJ cartilage from six patients with lumbar degenerative disease (degeneration grades I, II, and III grouped as FJ1, FJ2, and FJ3, respectively) using scRNA-seq (Figure 1A and Table 1). After quality control and extensive filtration, 30418 individual cells were selected for sequencing and subsequent analysis. A total of 2646 DEGs were identified to classify cell populations in human LFJ cartilage (Supple
| Sample | LFJ degeneration grade | Group | Age | Gender |
| FJ1_1 | I | FJ1 | 39 | Male |
| FJ2_1 | II | FJ2 | 65 | Male |
| FJ3_1 | III | FJ3 | 68 | Female |
| FJ1_2 | I | FJ1 | 51 | Male |
| FJ2_2 | II | FJ2 | 69 | Female |
| FJ3_2 | III | FJ3 | 78 | Female |
| Immunohistochemistry | ||||
| FJ1_3 | I | - | 49 | Male |
| FJ3_3 | III | - | 65 | Female |
The chondrocytes exhibited high expression of the integrin-related gene ITGBL1, the extracellular secretion-related and interaction-related genes FBLN1, FNDC1, and SULF1, and the collagen binding activity-related gene LRRC15 (Figure 1C and Supplementary Figure 1A). Macrophages were identified in LFJs of all degeneration grades using the markers LILRB5, C1QC, FPR3, MSR1, and FOLR2 (Figure 1C and Supplementary Figure 1B). A violin plot was used to illustrate the expression of representative genes of chondrocytes and macrophages (Supplementary Figure 1). Monocytes were identified by the markers CD300E, LGALS2, CLEC10A, CLEC7A, and EPB41 L3. HSCs exhibited high expression of AVP, HTR1F, and CRYGD, and endothelial cells exhibited high expression of CD300 LG, TM4SF18, KDR, TEK, and ADGRL4. Erythroid cells were identified by expression of CTSE, ART4, and TSPO2. Other cells involved in immune regulation, such as granulocyte-monocyte progenitors, T cells, B cells, DCs, mast cells, neutrophils, plasma cells, and pDCs, were also identified on the basis of marker gene expression (Figure 1C and Supplementary Table 4).
Additionally, we analyzed the proportion and distribution of each cell cluster (Figure 1D) in mild degenerated (FJ1) and severe degenerated (FJ2 and FJ3) LFJs. Compared with mild degenerated LFJs, the percentages of neutrophils, HSCs, and B cells were lower, while those of chondrocytes, macrophages, monocytes, and T cells were significantly higher, in severe degenerated LFJs.
To determine the heterogeneity of LFJ cartilage and the functions of chondrocyte subpopulations during LFJ dege
The GO and KEGG pathway enrichment analysis showed that the proCs were primarily enriched in the regulation of chondrocyte proliferation and metabolism (Figure 3A and B), as expected, while preHTCs were preferentially focused on differentiation, skeletal-muscle regeneration, biosynthesis, and secretion. HTCs were preferentially involved in chondro
The proportion and distribution of the chondrocyte subclusters in mild and severe degenerated LFJs are shown in Figure 3C. There were no significant variations in the distributions of preHTCs, HTCs, and RegCs. By contrast, the proportions of proCs and proinCs were significantly higher in mild degenerated LFJs than in severe degenerated LFJs, indicating a more pronounced process of chondrocyte proliferation and immune response regulation in the mild dege
To further explore the relationship and cell lineage trajectories of the chondrocyte subclusters, we performed RNA velocity analysis. The pseudotime trajectory axis aligned well with the chondrocyte subclusters. As shown in Figure 3D, the proCs were followed by preHTCs and HTCs, and most FCs were mainly distributed at the end of the pseudotime trajectory. ECs and RegCs were distributed along the pseudotime trajectory axis, and proinCs were mainly positioned at the beginning, but not at the end, of the trajectory. HomCs formed a small branch at approximately the midline of the axis, suggesting a potential differentiation branch of chondrocytes.
Results shown in Figure 1D indicated that the proportion of macrophages was significantly higher in severe degenerated LFJs than in mild degenerated LFJs. Further investigation led to the identification of five distinct macrophage subclusters (Mφ-F13A1, Mφ-TCN2, Mφ-COL27A1, Mφ-CD52, and Mφ-IL3RA) based on their transcriptomic profiles and cluster-specific DEGs (Figure 4 and Supplementary Table 6). Mφ-F13A1 expressed F13A1, LYVE1, AREG, CCL8, and NR4A3, while Mφ-TCN2 showed high expression of TCN2, LINC01094, BHLHE41, MYO1G, and ADAM8. Mφ-COL27A1 was cha
The immunohistochemical assays of the macrophage marker CD68 confirmed the presence of macrophages in dege
To further investigate the functions of the macrophage subclusters in LFJ degeneration, gene set variation analysis of the GO and KEGG signaling pathways was performed. Further analysis revealed distinct transcriptional and functional characteristics across the five macrophage subclusters. Mφ-F13A1 exhibited a relatively pro-inflammatory transcriptional profile, whereas Mφ-TCN2 was associated with phagocytosis and cell adhesion-related processes. Mφ-COL27A1 showed enrichment of mitochondrial and oxidative phosphorylation-related programs, while Mφ-CD52 was associated with metabolic processes including cGMP and biotin metabolism. In contrast, Mφ-IL3RA exhibited enrichment in neutrophil regulation, mineral absorption, and mTOR, FoxO, EGFR, and PI3K-AKT signaling pathways (Figure 4E and Figure 5A and B). These findings indicate that the five macrophage subpopulations possess distinct functional characteristics during LFJ degeneration.
The proportion and distribution of each macrophage subcluster in mild and severe degenerated LFJs are shown in Figure 5C. In severe degenerated LFJs, the proportions of Mφ-F13A1 and Mφ-TCN2 were increased, while the proportion of Mφ-CD52 was significantly decreased. Notably, Mφ-IL3RA was not detected in mild degenerated LFJs, but was identified in severe degenerated LFJs, indicating that it may play a crucial role in severe degeneration (Figure 5C and D). RNA velocity analysis showed a clear directional flow from Mφ-CD52 toward Mφ-TCN2, then toward Mφ-F13A1 and Mφ-IL3RA (Figure 5E). Based on the functional characteristics of macrophages, these findings suggest that macrophages are likely involved in the processes of inflammation and tissue regeneration regulation during LFJ degeneration. Notably, the strength and number of chondrocyte-macrophage interactions increased progressively with cartilage degeneration. Compared with mild degenerated LFJs, severe degenerated LFJs exhibited enhanced intercellular communication, with grade III showing the most prominent changes. These findings suggest that chondrocyte-macrophage crosstalk is pro
To further investigate the intercellular interactions between chondrocytes and other cell clusters during human LFJ degeneration, we performed cell-cell communication network analysis on the basis of the ligand-receptor interactions using the CellChat R package. The interacting receptors and ligands identified in different cell clusters are listed in Supplementary Table 2. The differential numbers of interactions between chondrocytes and inflammation-related cell clusters, such as macrophages, B cells, mast cells, erythroid cells, and T cells, were significantly higher in severe degene
The signaling pathways of intercellular interactions were further explored (Supplementary Figure 2). The signaling pathways involving collagen, fibronectin 1 (FN1), osteopontin (SPP1), laminin, C-X-C motif chemokine ligand (CXCL), CD99, midkine, and galectin were found to be active in all LFJ degeneration grades (Figure 7A). The signaling pathway network showed that the communication of chondrocytes with other cells mainly relies on the signaling pathways of collagen, FN1, CXCL, laminin, amyloid precursor protein, CD99, SPP1, pleiotrophin, notch, thrombospondin, macro
As a frequent form of LFJ syndrome and a common cause of LBP, FJOA imposes an enormous health and economic burden on society[31]. Considering that neither conservative strategies nor invasive surgical procedures have provided permanent pain relief[4], new strategies for the diagnosis and treatment of FJOA are urgently needed. Because of the limited research on FJOA, the cellular heterogeneity of LFJ and the complex pathophysiological process of LFJ dege
Articular cartilage homeostasis is of critical importance for the physiological functioning of facet joints. FJOA is primarily characterized by the degeneration of small joint cartilage followed by inflammation and degradation[4]. Cartilage, comprising chondrocytes and extracellular matrix, is considered a physiologically non-self-renewing avascular tissue[32]. To better understand the pathophysiological mechanisms underlying the role of chondrocytes in the deve
The identification of proinCs in this study warrants careful interpretation. Rather than being defined by a single canonical marker, proinCs were characterized by a coordinated inflammatory transcriptional program, as evidenced by the preferential expression of CXCL3, CXCL1, CXCL2, and IL6, together with the enrichment of Toll-like receptor and phosphatidylinositol signaling pathways. These features collectively support the annotation of proinCs as an inflammation-associated chondrocyte subcluster. Nevertheless, because the annotation of proinCs was primarily based on transcriptomic profiles, these cells should be interpreted as a transcriptomically defined inflammatory chondrocyte subcluster or cellular state rather than a definitively established independent lineage. Further spatial and functional studies will be required to determine whether this transcriptional state corresponds to a stable and biologically distinct chondrocyte population and to clarify its contribution to LFJ degeneration.
The distribution of chondrocyte populations and their differential trajectories at different stages offers insights into the changing characteristics of cartilage and chondrocytes during the LFJ degeneration process. As expected, the proportion of proCs was significantly higher in mild degenerated LFJs than in severe degenerated LFJs, whereas the proportion of preHTCs showed the opposite trend. The differentiation trajectory of chondrocytes was basically consistent with previous studies of osteoarthritis, exhibiting a route from proCs to preHTCs and HTCs, and finally to FCs[9,33]. There were no significant differences in the distributions of HomCs, FCs, and RegCs during the different degeneration stages. The trajectories of ECs and RegCs were distributed along the pseudotime axis, indicating that their roles are interwoven throughout the entire progression of LFJ degeneration. By contrast, we found a higher proportion of proinCs in mildly degenerated LFJs than in severely degenerated LFJs. Additionally, proinCs were mainly distributed at the beginning of the inferred trajectory rather than at the terminal stage, predicting that this inflammatory chondrocyte state may be more closely associated with the early stage of LFJ degeneration. The prominence of proinCs during early degeneration may reflect an initial inflammatory response to joint stress. These findings raise the possibility that modulating proinC-associated inflammatory signaling during early degeneration may represent a potential research direction, although this hypothesis requires functional validation.
Inflammatory reactions and immune system changes are known to be important components of facetogenic LBP and the pathogenesis of osteoarthritis[35,36]. Research on osteoarthritis has revealed that the innate and adaptive immune cell types that contribute to the disease pathogenesis include macrophages, neutrophils, B cells, T cells, and DCs[37,38]. Data from scRNA-seq studies have revealed changes in the immune landscape in osteoarthritis[39]; however, the immune landscape and precise role of immunocyte subtypes in the initiation or progression of LFJ degeneration remain poorly understood. Here, we illustrated the immune landscape and changes in immunocyte subtypes during different degene
Macrophages reportedly act as immune cells with critical importance in the symptomatology and structural pro
Cellular communications and molecular interactions among chondrocytes, immune cells, and various other cell types in the joint microenvironment may influence intra-articular degradation and degeneration, or repair and regeneration, and are crucial to understanding the pathogenesis and development of FJOA[39]. Research has shown that paracrine interactions between immunocytes and chondrocytes, such as the secretion of inflammatory cytokines, growth factors, matrix metalloproteinases, and tissue inhibitors of metalloproteinases, lead to cartilage degeneration and play a vital role in the development of OA[41]. Our study showed that the intercellular interactions between chondrocytes and other cells exhibited varying quantities and strength across different degeneration grades, with a notable enhancement of chondro
This exploratory study provides a single-cell atlas of human LFJ cartilage and identifies stage-associated chondrocyte and macrophage subpopulations, along with predicted intercellular communication patterns. The enhanced chondrocyte-macrophage crosstalk identified during degeneration provides new insights into the cellular mechanisms underlying LFJ degeneration and offers testable hypotheses for future investigation.
This study has several limitations. First, the sample size, while informative for this initial investigation, remains modest. Furthermore, the analysis was confined to cartilage tissue, omitting the synovium and subchondral bone which are relevant to joint degeneration. Most critically, the functional roles of the novel cell clusters we identified are computationally inferred and await experimental validation in FJOA models. In conclusion, our study findings illustrate the landscape and heterogeneity of LFJ cartilage at single-cell resolution, showing the development of major cell subclusters and functional distinctions at the different degeneration grades of LFJ. Cell communications between chondrocytes and other cell clusters in the microenvironment further revealed the signaling pathways and mechanisms of LFJ degeneration. These findings expand our knowledge of the biological basis and pathophysiology of this widespread disorder, opening possibilities for the re-establishment of homeostasis to prevent LFJ degeneration and facilitating the development of improved diagnostic and treatment strategies for FJOA.
| 1. | Gellhorn AC, Katz JN, Suri P. Osteoarthritis of the spine: the facet joints. Nat Rev Rheumatol. 2013;9:216-224. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 406] [Cited by in RCA: 320] [Article Influence: 24.6] [Reference Citation Analysis (0)] |
| 2. | Cascio MA, Dua A, Varacallo MA. Lumbosacral Facet Syndrome. 2025 Jul 6. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan-. [PubMed] |
| 3. | Akar E, Somay H. Comparative morphometric analysis of congenital and acquired lumbar spinal stenosis. J Clin Neurosci. 2019;68:256-261. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 6] [Cited by in RCA: 16] [Article Influence: 2.3] [Reference Citation Analysis (0)] |
| 4. | Du R, Xu G, Bai X, Li Z. Facet Joint Syndrome: Pathophysiology, Diagnosis, and Treatment. J Pain Res. 2022;15:3689-3710. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 10] [Cited by in RCA: 35] [Article Influence: 8.8] [Reference Citation Analysis (0)] |
| 5. | Mann SJ, Viswanath O, Singh P. Lumbar Facet Arthropathy. 2023 Jul 4. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-. [PubMed] |
| 6. | Kapetanakis S, Gkantsinikoudis N. Anatomy of lumbar facet joint: a comprehensive review. Folia Morphol (Warsz). 2021;80:799-805. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1] [Cited by in RCA: 27] [Article Influence: 4.5] [Reference Citation Analysis (0)] |
| 7. | Li J, Muehleman C, Abe Y, Masuda K. Prevalence of facet joint degeneration in association with intervertebral joint degeneration in a sample of organ donors. J Orthop Res. 2011;29:1267-1274. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 45] [Cited by in RCA: 44] [Article Influence: 2.9] [Reference Citation Analysis (0)] |
| 8. | Ko S, Vaccaro AR, Lee S, Lee J, Chang H. The prevalence of lumbar spine facet joint osteoarthritis and its association with low back pain in selected Korean populations. Clin Orthop Surg. 2014;6:385-391. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 30] [Cited by in RCA: 35] [Article Influence: 2.9] [Reference Citation Analysis (0)] |
| 9. | Ji Q, Zheng Y, Zhang G, Hu Y, Fan X, Hou Y, Wen L, Li L, Xu Y, Wang Y, Tang F. Single-cell RNA-seq analysis reveals the progression of human osteoarthritis. Ann Rheum Dis. 2019;78:100-110. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 291] [Cited by in RCA: 391] [Article Influence: 55.9] [Reference Citation Analysis (9)] |
| 10. | Shen P, Löhning M. Insights into osteoarthritis development from single-cell RNA sequencing of subchondral bone. RMD Open. 2022;8:e002617. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in RCA: 5] [Reference Citation Analysis (0)] |
| 11. | Zhang X, Huang N, Huang R, Wang L, Ke Q, Cai L, Wu S. Single-cell rna seq analysis identifies the biomarkers and differentiation of chondrocyte in human osteoarthritis. Am J Transl Res. 2020;12:7326-7339. [PubMed] |
| 12. | Huang ZY, Luo ZY, Cai YR, Chou CH, Yao ML, Pei FX, Kraus VB, Zhou ZK. Single cell transcriptomics in human osteoarthritis synovium and in silico deconvoluted bulk RNA sequencing. Osteoarthritis Cartilage. 2022;30:475-480. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 10] [Cited by in RCA: 48] [Article Influence: 12.0] [Reference Citation Analysis (0)] |
| 13. | Wu C, Yu J, Xu G, Bao G, Zhang J, Xue P, Jiang J, Chen J, Chen C, Hong H, Cui Z. Wnt16 protects chondrocytes from lumbar facet joint osteoarthritis through the Wnt/β-catenin pathway in low back pain patients. Somatosens Mot Res. 2021;38:339-346. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1] [Cited by in RCA: 6] [Article Influence: 1.2] [Reference Citation Analysis (0)] |
| 14. | Chen C, Bao GF, Xu G, Sun Y, Cui ZM. Altered Wnt and NF-κB Signaling in Facet Joint Osteoarthritis: Insights from RNA Deep Sequencing. Tohoku J Exp Med. 2018;245:69-77. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 18] [Cited by in RCA: 27] [Article Influence: 3.4] [Reference Citation Analysis (1)] |
| 15. | Zhao J, Zheng L, Dai G, Sun Y, He R, Liu Z, Jin Y, Wu T, Hu J, Cao Y, Duan C. Senolytics cocktail dasatinib and quercetin alleviate chondrocyte senescence and facet joint osteoarthritis in mice. Spine J. 2025;25:184-198. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 23] [Reference Citation Analysis (0)] |
| 16. | Huang C, Zeng B, Zhou B, Chen G, Zhang Q, Hou W, Xiao G, Duan L, Hong N, Jin W. Single-cell transcriptomic analysis of chondrocytes in cartilage and pathogenesis of osteoarthritis. Genes Dis. 2025;12:101241. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 19] [Reference Citation Analysis (0)] |
| 17. | Raut RD, Chakraborty AK, Neogi T, Albro M, Snyder B, Schaer TP, Zhang C, Grinstaff MW, Bais MV. A multi-tissue human knee single-cell atlas identifies that osteoarthritis reduces regenerative tissue stem cells while increasing inflammatory pain macrophages. Commun Biol. 2025;8:1146. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in RCA: 27] [Reference Citation Analysis (1)] |
| 18. | Huang ZY, Luo ZY, Cai YR, Chou CH, Yao ML, Pei FX, Kraus VB, Zhou ZK. Single cell transcriptomics in human osteoarthritis synovium and in silico deconvoluted bulk RNA sequencing. Osteoarthritis Cartilage. 2022;30:475-480. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 10] [Cited by in RCA: 48] [Article Influence: 12.0] [Reference Citation Analysis (0)] |
| 19. | Liu M, Wu C, Wu C, Zhou Z, Fang R, Liu C, Ning R. Immune cells differentiation in osteoarthritic cartilage damage: friends or foes? Front Immunol. 2025;16:1545284. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 15] [Reference Citation Analysis (0)] |
| 20. | Sebastian A, Hum NR, McCool JL, Wilson SP, Murugesh DK, Martin KA, Rios-Arce ND, Amiri B, Christiansen BA, Loots GG. Single-cell RNA-Seq reveals changes in immune landscape in post-traumatic osteoarthritis. Front Immunol. 2022;13:938075. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in RCA: 90] [Reference Citation Analysis (1)] |
| 21. | Weishaupt D, Zanetti M, Boos N, Hodler J. MR imaging and CT in osteoarthritis of the lumbar facet joints. Skeletal Radiol. 1999;28:215-219. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 338] [Cited by in RCA: 389] [Article Influence: 14.4] [Reference Citation Analysis (2)] |
| 22. | Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36:411-420. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 7652] [Cited by in RCA: 9451] [Article Influence: 1181.4] [Reference Citation Analysis (9)] |
| 23. | McGinnis CS, Murrow LM, Gartner ZJ. DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell Syst. 2019;8:329-337.e4. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 3911] [Cited by in RCA: 3496] [Article Influence: 499.4] [Reference Citation Analysis (7)] |
| 24. | Haghverdi L, Lun ATL, Morgan MD, Marioni JC. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol. 2018;36:421-427. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1496] [Cited by in RCA: 1700] [Article Influence: 212.5] [Reference Citation Analysis (5)] |
| 25. | Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, Chak S, Naikawadi RP, Wolters PJ, Abate AR, Butte AJ, Bhattacharya M. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol. 2019;20:163-172. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 4331] [Cited by in RCA: 3824] [Article Influence: 546.3] [Reference Citation Analysis (11)] |
| 26. | Mabbott NA, Baillie JK, Brown H, Freeman TC, Hume DA. An expression atlas of human primary cells: inference of gene function from coexpression networks. BMC Genomics. 2013;14:632. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 341] [Cited by in RCA: 380] [Article Influence: 29.2] [Reference Citation Analysis (0)] |
| 27. | Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, Fu X, Liu S, Bo X, Yu G. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb). 2021;2:100141. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 5856] [Cited by in RCA: 7172] [Article Influence: 1434.4] [Reference Citation Analysis (7)] |
| 28. | Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, Lennon NJ, Livak KJ, Mikkelsen TS, Rinn JL. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32:381-386. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 6688] [Cited by in RCA: 5477] [Article Influence: 456.4] [Reference Citation Analysis (7)] |
| 29. | La Manno G, Soldatov R, Zeisel A, Braun E, Hochgerner H, Petukhov V, Lidschreiber K, Kastriti ME, Lönnerberg P, Furlan A, Fan J, Borm LE, Liu Z, van Bruggen D, Guo J, He X, Barker R, Sundström E, Castelo-Branco G, Cramer P, Adameyko I, Linnarsson S, Kharchenko PV. RNA velocity of single cells. Nature. 2018;560:494-498. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 2712] [Cited by in RCA: 3078] [Article Influence: 384.8] [Reference Citation Analysis (6)] |
| 30. | Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R. CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat Protoc. 2020;15:1484-1506. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 3384] [Cited by in RCA: 2509] [Article Influence: 418.2] [Reference Citation Analysis (3)] |
| 31. | Perolat R, Kastler A, Nicot B, Pellat JM, Tahon F, Attye A, Heck O, Boubagra K, Grand S, Krainik A. Facet joint syndrome: from diagnosis to interventional management. Insights Imaging. 2018;9:773-789. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 73] [Cited by in RCA: 144] [Article Influence: 18.0] [Reference Citation Analysis (0)] |
| 32. | Jiang Y, Tuan RS. Origin and function of cartilage stem/progenitor cells in osteoarthritis. Nat Rev Rheumatol. 2015;11:206-212. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 206] [Cited by in RCA: 310] [Article Influence: 25.8] [Reference Citation Analysis (2)] |
| 33. | Hu X, Li Z, Ji M, Lin Y, Chen Y, Lu J. Identification of cellular heterogeneity and immunogenicity of chondrocytes via single-cell RNA sequencing technique in human osteoarthritis. Front Pharmacol. 2022;13:1004766. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in RCA: 22] [Reference Citation Analysis (0)] |
| 34. | Lande R, Giacomini E, Serafini B, Rosicarelli B, Sebastiani GD, Minisola G, Tarantino U, Riccieri V, Valesini G, Coccia EM. Characterization and recruitment of plasmacytoid dendritic cells in synovial fluid and tissue of patients with chronic inflammatory arthritis. J Immunol. 2004;173:2815-2824. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 105] [Cited by in RCA: 115] [Article Influence: 5.2] [Reference Citation Analysis (0)] |
| 35. | Kim JS, Ali MH, Wydra F, Li X, Hamilton JL, An HS, Cs-Szabo G, Andrews S, Moric M, Xiao G, Wang JH, Chen D, Cavanaugh JM, Im HJ. Characterization of degenerative human facet joints and facet joint capsular tissues. Osteoarthritis Cartilage. 2015;23:2242-2251. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 43] [Cited by in RCA: 53] [Article Influence: 4.8] [Reference Citation Analysis (0)] |
| 36. | Woodell-May JE, Sommerfeld SD. Role of Inflammation and the Immune System in the Progression of Osteoarthritis. J Orthop Res. 2020;38:253-257. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 133] [Cited by in RCA: 312] [Article Influence: 52.0] [Reference Citation Analysis (0)] |
| 37. | Hsueh MF, Zhang X, Wellman SS, Bolognesi MP, Kraus VB. Synergistic Roles of Macrophages and Neutrophils in Osteoarthritis Progression. Arthritis Rheumatol. 2021;73:89-99. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 33] [Cited by in RCA: 133] [Article Influence: 22.2] [Reference Citation Analysis (0)] |
| 38. | Zhu W, Zhang X, Jiang Y, Liu X, Huang L, Wei Q, Huang Y, Wu W, Gu J. Alterations in peripheral T cell and B cell subsets in patients with osteoarthritis. Clin Rheumatol. 2020;39:523-532. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 17] [Cited by in RCA: 50] [Article Influence: 7.1] [Reference Citation Analysis (0)] |
| 39. | Sebastian A, Hum NR, McCool JL, Wilson SP, Murugesh DK, Martin KA, Rios-Arce ND, Amiri B, Christiansen BA, Loots GG. Single-cell RNA-Seq reveals changes in immune landscape in post-traumatic osteoarthritis. Front Immunol. 2022;13:938075. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in RCA: 90] [Reference Citation Analysis (1)] |
| 40. | Netzer C, Urech K, Hügle T, Benz RM, Geurts J, Schären S. Characterization of subchondral bone histopathology of facet joint osteoarthritis in lumbar spinal stenosis. J Orthop Res. 2016;34:1475-1480. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 22] [Cited by in RCA: 25] [Article Influence: 2.5] [Reference Citation Analysis (0)] |
| 41. | Zhang H, Cai D, Bai X. Macrophages regulate the progression of osteoarthritis. Osteoarthritis Cartilage. 2020;28:555-561. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 509] [Cited by in RCA: 464] [Article Influence: 77.3] [Reference Citation Analysis (4)] |