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Fibro-NPC: a pathogenic subtype identified at single-cell resolution with secreted SFRP4 as a biomarker in intervertebral disc degeneration

Journal of Translational Medicine volume 23, Article number: 867 (2025) Cite this article

Abstract

Background

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Intervertebral disc degeneration (IDD) is a primary cause of low back pain and, in severe cases, can lead to disability. Current treatments for low back pain remain limited in efficacy, underscoring the need for a deeper understanding of the molecular mechanisms driving IDD. The degeneration process is primarily driven by an imbalance in the extracellular matrix, largely due to the senescence of nucleus pulposus cells (NPCs).

Methods

Through single-cell sequencing of degenerated nucleus pulposus tissue from five intervertebral discs, we identified five distinct NPC subtypes. Notably, fibrosis-associated NPCs (Fibro-NPC) were predominantly observed at the terminal stage of cell differentiation, identifying Fibro-NPC as a pathogenic subtype in IDD. To further explore intercellular interactions, we used the CellChat algorithm to construct a cell communication network encompassing the diverse cell types in the nucleus pulposus. Mass spectrometry analysis of normal and degenerated tissue subsequently identified seven core proteins associated with IDD. Among these, WGCNA and machine learning highlighted SFRP4 as a central pathogenic protein, highly expressed in Fibro-NPC.

Results

Advanced differentiation of nucleus pulposus cells, particularly the Fibro-NPC subtype, is associated with the secretion of SFRP4, which accelerates cellular senescence. This senescence contributes to fibrosis within the nucleus pulposus, along with angiogenesis and inflammatory infiltration in the nucleus pulposus microenvironment. Collectively, these processes drive intervertebral disc degeneration.

Conclusions

Our findings position SFRP4 as a biomarker for IDD, presenting a novel target for its diagnosis and treatment.

Graphical Abstract

Background

IDD is a major contributor to the global burden of low back pain, placing significant strain on healthcare systems worldwide [1, 2]. Currently, clinical treatment for IDD primarily involves conservative drug therapy and surgical intervention [3]. However, understanding the molecular mechanisms underlying IDD is essential for developing more effective therapies. Aging of NPCs has been identified as a primary factor contributing to IDD [4]. This aging process is characterized by cellular stagnation and an imbalance in the extracellular matrix [5, 6]. Moreover, the sustained secretion of the senescence-associated secretory phenotype (SASP) by aging NPCs fosters an inflammatory microenvironment, further exacerbating SASP secretion [7].

Previous analysis of extensive single-cell sequencing data from the nucleus pulposus has classified NPCs into distinct clusters based on their functional roles and differentiation states. These clusters include Pre-NPC, Reg-NPC, EF-NPC, Hom-NPC, and Fibro-NPC [8, 9]. Each NPC subtype exhibits different states of differentiation and distinct biological functions within the nucleus pulposus. Furthermore, their interactions with immune cells (IC) [9] and endothelial cells (EC) within the nucleus pulposus microenvironment, as well as the composition of the signaling networks among the various NPC subtypes, are critical for understanding the molecular mechanisms underlying IDD.

Label-free quantitative proteomics is an effective method for analyzing protein dynamics across a diverse array of samples and for identifying significantly differentially expressed proteins within cells [10]. The combined application of weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression is a widely adopted approach for screening key genes and proteins associated with diseases [11, 12]. This integrated strategy provides crucial support for identifying core proteins implicated in IDD.

SFRP4, a member of the secreted frizzled-related protein (SFRP) family, plays a crucial role in regulating the canonical WNT signaling pathway [13]. It influences a wide range of biological functions, particularly in the context of tumors, metabolic diseases, and degenerative tissue disorders [14,15,16]. However, evidence regarding the role of SFRP4 in IDD remains limited.

In light of the aforementioned research, we analyzed single-cell sequencing data from five cases of degenerative nucleus pulposus obtained from the GEO database. Our findings indicate that Fibro-NPC is predominantly present at the terminal stage of NPC differentiation and represents a pathogenic subtype. We utilized the CellChat [17] tool to elucidate the signaling network interactions among pathogenic subtype NPCs, other NPC subtypes, IC, and EC. To identify proteins critical to IDD, we collected nucleus pulposus tissue samples from 20 patients, including 10 cases of normal tissue and 10 cases of degenerated tissue. We then conducted label-free quantitative proteomics analysis to identify differentially expressed proteins between degenerated and normal nucleus pulposus tissues. Through the application of weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression, we identified SFRP4 as a key protein secreted during nucleus pulposus degeneration. Our time series analysis revealed that SFRP4 is predominantly expressed at the final stages of NPC differentiation. Furthermore, we demonstrated that downregulation of SFRP4 expression can mitigate the senescence of nucleus pulposus cells. In conclusion, we provide a comprehensive elucidation of the pathogenic role of Fibro-NPC in IDD, highlighting that the secretion of SFRP4, a core protein in this process, is closely associated with the senescence of NPCs.

Methods

Single-cell RNA sequencing

Single-cell RNA sequencing data of degenerating nucleus pulposus cells were obtained from the Gene Expression Omnibus (GEO) database, including GSM7831817, GSM7831818, GSM7831819 in GSE244889 and GSM7432172 and GSM7432173 in GSE233666. All five patients had a Pfirrmann grade of 3 or higher. The scRNA-seq data were processed using the R package “Seurat”. From the 5 samples containing a total of 32,758 cells, cells with mitochondrial gene content exceeding 10% were excluded, resulting in a final dataset of 26,698 cells. Since the samples originate from different datasets, the PCA algorithm and the R package “Harmony” were used for batch effect correction, while Uniform Manifold Approximation and Projection (UMAP) was applied for dimensionality reduction and cluster identification [18]. Cell marker genes for cellular annotation were primarily referenced from the CellMarker database (http://xteam.xbio.top/CellMarker/), while ICs were annotated using the R package “SingleR”. The R package “Monocle” was employed to construct the pseudotime trajectory of NPCs. Additionally, molecular interaction networks between different cell types were explored using CellChat.

Mass spectrometry

We collected nucleus pulposus tissues from 20 patients, including 10 control NP tissues and 10 degenerated NP tissues. This project was analyzed using next-generation label-free quantitative proteomics technology. In Data Independent Acquisition (DIA) mode, it can deliver unprecedented proteomic coverage while enabling accurate and highly repeatable quantification for large amounts of proteins per sample. First, the samples were subjected to SDS-PAGE for protein separation, followed by proteolysis. The resulting peptides were desalted using a Strata X column, then separated by high-pH reversed-phase liquid chromatography. Peptide detection was carried out using a timsTOF Pro mass spectrometer in both DDA (Data Dependent Acquisition) and DIA (Data Independent Acquisition) modes. In DDA mode, peptide spectra from the samples are acquired to build a spectral library containing peptide features such as fragmentation ion intensities and retention times. In DIA mode, peptide information is captured using a wide precursor ion window, enabling the quantitative detection of highly abundant peptides and proteins. The mass spectrometry data were deconvoluted and integrated with the DDA spectral library for qualitative and quantitative analysis of the DIA data. Protein data were then subjected to gene annotation and differential analysis using the R packages “MSstats” and “limma,” alongside the UniProt database (www.uniprot.org/).

WGCNA

WGCNA is a widely used bioinformatics method for exploring relationships between genes or proteins and phenotypic traits [19]. We applied WGCNA to construct weighted co-expression networks for protein expression data. A soft threshold (power = 20, R² = 0.89) was selected based on the principle of a scale-free network to generate a scale-free co-expression network. The adjacency matrix was then transformed into a topological overlap matrix. Cluster analysis identified distinct modules, with a minimum module size of 30 proteins. Hierarchical clustering was used to create a dendrogram and calculate the correlation between module eigengenes and disease phenotypes. The modules with the highest correlation coefficients and lowest p-values were defined as disease-associated. The turquoise module exhibited the strongest correlation.

Machine learning

LASSO regression is a machine learning algorithm that integrates variable selection with regularization to control model complexity [20]. When fitting generalized linear models, LASSO controls model complexity by adjusting the parameter λ. As λ increases, variables are penalized more, leading to the retention of only a small number of key representative genes or proteins. In this study, we intersected the proteins from the modules with the highest correlation coefficients identified by WGCNA with the differentially expressed proteins, and applied LASSO regression to these intersected proteins using the R package “glmnet”. The optimal λ value was determined through 10-fold cross-validation, and key proteins associated with IDD were selected based on the minimum criteria. The R package “pROC” was then used to analyze the ROC curves of the key proteins from the LASSO regression analysis, with the area under the curve (AUC) and 95% confidence interval (CI) calculated. The significance of the model for IDD was evaluated by the AUC, with values closer to 1 indicating higher predictive accuracy.

GSEA

Gene set enrichment analysis (GSEA) employs an integrative approach to analyze the entire genome, ranking genes based on their differential expression in a specific phenotype or disease state. This analysis is enhanced by the use of predefined gene sets, such as pathways or gene ontologies, which facilitate a deeper understanding of the dysregulated biological processes involved [21]. GSEA identifies whether these predefined gene sets are enriched at the extremes of the ranked list, thereby assessing the significant association of relevant pathways or gene ontologies with the phenotype under investigation. In this study, we utilized the R package “ReactomePA“ [22] for GSEA to analyze the differential signaling pathways between the degenerative and normal groups, aiming to explore the potential mechanisms of proteins in these two contexts.

Gene ontology (GO) and reactome analysis

A Gene Ontology (GO) and Reactome enrichment analysis of genes and proteins was performed using Metascape (https://metascape.org/). Terms and pathways were considered statistically significant when the q-value was less than 0.01.

Primary isolation and in vitro culture of human NPCs

Nucleus pulposus tissue from patients with burst lumbar fractures and lumbar disc herniations was excised under sterile conditions and digested with 0.5% type II collagenase at 37 °C for 12 h. The suspension was centrifuged at 1500 rpm for 5 min, and the pellet was resuspended in DMEM/F-12 medium containing 10% fetal bovine serum. Cells were cultured at 37 °C with 5% CO₂ and transferred to 10 cm Petri dishes at an appropriate density. The medium was changed every three days, and the first three cell passages were used for experiments. NPCs were seeded into 6-well plates, and upon reaching 80% confluence, they were treated with 2 µg/ml rhSFRP4 (MCE HY-P74551), or 40 ng/ml IL1β for 72 h for subsequent experiments.

Western blotting analysis

Proteins from treated NPCs were extracted and separated by SDS-PAGE using 7.5%, 10%, or 12.5% gels. The membrane was blocked with 2.5% bovine serum albumin (BSA) and incubated with primary antibodies. After washing with PBS, the membrane was incubated with secondary antibodies: anti-rabbit IgG (1:5000, 7074, Cell Signaling Technology) or anti-mouse IgG (1:5000, 7076, Cell Signaling Technology). Protein bands were visualized using enhanced chemiluminescence detection reagents (Invitrogen, CA, USA).

In vitro small interfering RNA (siRNA) transfection

SiRNA targeting SFRP4 was synthesized by GenePharma (Shanghai, P.R.China). Human nucleus pulposus cells were seeded into six- and 48-well plates. When cell confluence reached 60–70%, 50 nM of either negative control siRNA (Si-Ctrl) or SFRP4 siRNA was transfected using Lipofectamine 3000 (Invitrogen), following the manufacturer’s instructions. After 48 h, cells in six-well plates were harvested for target gene expression analysis, while those in 48-well plates underwent immunofluorescence staining to assess SFRP4 expression levels.

Immunofluorescence analysis

After a 24-hour growth period on confocal culture plates, nucleus pulposus cells (NPCs) were processed according to experimental requirements and incubated for an additional 24 h. The cells were then fixed with 4% formaldehyde for 15 min and permeabilized with 0.1% Triton X-100 for 10 min. Following a washing step, the cells were blocked with 10% goat serum for one hour. Subsequently, they were incubated with the primary antibody and treated with goat anti-rabbit or anti-mouse IgG. The nuclei were stained with 4’,6-diamidino-2-phenylindole (DAPI; Solarbio, C0065).

Immunohistochemistry

Tissue samples were embedded in paraffin and sectioned to a thickness of 5 μm. The sections were dewaxed and rehydrated, followed by antigen retrieval using 0.01 M sodium citrate. Blocking was performed with 3% hydrogen peroxide and 5% normal goat serum. The slides were then incubated with the appropriate primary antibodies. Following this incubation, the sections received treatment with a secondary antibody, were stained with DAB solution, and the nuclei were counterstained with hematoxylin. Finally, the samples were observed and imaged, and the percentage of positive cells in the intervertebral disc (IVD) samples was quantified using ImageJ software.

β-gal cell senescence staining

Cellular senescence in NPCs was evaluated using a β-galactosidase staining kit. The procedure involved adding 1 mL of fixative solution to the NPCs and incubating for 15 min at room temperature. Following this, the staining working solution was prepared according to the manufacturer’s instructions, and the NPCs were incubated with the staining solution at 37 °C overnight. The next day, stained cells were observed and quantified under a light microscope.

Statistical analysis

Data are presented as mean ± standard error of the mean (SEM). Validation of human and animal samples, both in vivo and in vitro, was conducted in at least three independent experimental replicates. Statistical analyses were performed using R software (version 4.4.0). Single-cell sequencing, WGCNA, LASSO regression, and GSEA analyses were also conducted using R. A p-value of < 0.01 was considered statistically significant, unless otherwise specified.

Results

scRNA-seq atlas of human NP tissues identifies five NPC subtypes

To elucidate the heterogeneity among nucleus pulposus cells, we conducted a comprehensive analysis of the single-cell sequencing data from the nucleus pulposus. scRNA-seq data from five cases of human degenerative nucleus pulposus, sourced from the GEO database, were analyzed. Using the R package “UMAP,” 26,698 cells were classified into four distinct cell types: nucleus pulposus cells (markers: ACAN, SOX9), endothelial cells (markers: KDR, VWF), smooth muscle cells (marker: ACTA2), and ICs (marker: PTPRC) (Fig. 1a and b). Subsequently, the R package “UMAP” was employed to further reduce the dimensionality of the identified nucleus pulposus cells, leading to the delineation of five distinct subtypes [8, 9] (Fig. 1c and d).

Fig. 1
figure 1

scRNA-seq atlas of human NP tissues identifies five NPC subtypes. (a) UMAP plot displaying four distinct cell types identified in five degenerated nucleus pulposus tissue samples: nucleus pulposus cells, immune cells, smooth muscle cells, and endothelial cells. (b) The UMAP plot illustrates the expression of marker genes associated with various cell types. (c) Single-cell mapping reveals the distribution of NPCs, and the diversity of cell types present in degenerated nucleus pulposus tissue. (d) Differentially expressed genes in each of the five nucleus pulposus cell subtypes are shown here

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Fibro-NPC represents a pathogenic subtype of degenerated nucleus pulposus

The five subtypes of nucleus pulposus cells were analyzed using the R package “Monocle” to perform pseudotime analysis [23]. The results indicate that Fibro-NPC predominantly resides at the terminal stage of nucleus pulposus cell differentiation [8] (Fig. 2a). The proportion of Hom-NPCs is highest during the initial stages of nucleus pulposus cell differentiation. Moreover, our findings illustrate that each stage of differentiation is distinguished by a unique proportion of particular NPC subtypes (Fig. 2b). The results of the proposed time series analysis revealed a gradual decline in the expression levels of aggrecan (ACAN) and collagen type II alpha 1 chain (COL2A1) throughout the differentiation process of nucleus pulposus cells (Fig. 2c and d). This finding is consistent with the observed aging process of NPCs [24]. The R package “scRNAtoolVis” was utilized to generate single-cell cluster volcano plots, illustrating differentially expressed genes across the distinct subtypes of NPCs (Fig. 2e). The analysis revealed a significant downregulation of Aggrecan (ACAN) and Collagen Type II Alpha 1 Chain (COL2A1), both of which are closely associated with IDD, specifically within the Fibro-NPC subtype. In contrast, there was a notable upregulation in the expression levels of MMP13, MMP9, MMP2, ADAMTS4, ADAMTS2, and COL1A1 [25]. The Reactome pathway enrichment analysis of the differentially expressed genes in the Fibro-NPC identified key associations with pathways involved in extracellular matrix organization and degradation, fibrotic processes, immune response modulation, and angiogenesis (Fig. 2f). Furthermore, six nucleus pulposus samples were obtained from young patients diagnosed with lumbar burst fractures or herniated discs. The degree of IDD in each patient was assessed using the Pfirrmann grading system based on magnetic resonance imaging (MRI), with the degeneration classified into grades G2, G3, or G4 (Fig. 2g). Western blot analysis demonstrated that with increasing degeneration grade, there was a notable decline in the expression of ACAN and COL2A1, accompanied by an elevated expression of MMP13, MMP9, MMP2, ADAMTS4, ADAMTS2, and COL1A1 (Fig. 2h). These findings indicate that IDD is accompanied by a progressive increase in the proportion of Fibro-NPC cells. It is therefore proposed that Fibro-NPC constitutes a pathogenic subtype implicated in the progression of IDD.

Fig. 2
figure 2

Fibro-NPC represents a pathogenic subtype of degenerated nucleus pulposus. (a) Pseudotime sequence analysis of the five subtypes of nucleus pulposus cells. (b) Proportion of five NPC subtypes at different stages of differentiation. (c) Pseudotime kinetics of ACAN in each subtype of NPCs. (d) Pseudotime kinetics of COL2A1 in each subtype of NPCs. (e) Single-cell volcano plots of five NPC subtypes, highlighting the expression of genes associated with IDD, including Collagen Type II Alpha 1 Chain (COL2A1), Aggrecan (ACAN), Collagen Type I Alpha 1 Chain (COL1A1), Matrix Metallopeptidase 13 (MMP13), Matrix Metallopeptidase 9 (MMP9), Matrix Metallopeptidase 2 (MMP2), A Disintegrin and Metalloproteinase with Thrombospondin Motifs 4 (ADAMTS4), and A Disintegrin and Metalloproteinase with Thrombospondin Motifs 2 (ADAMTS2). (f) Reactome enrichment analysis of differentially expressed genes in Fibro-NPCs (|Log2FC| >1, adjusted P < 0.05, pct. 1 > 0.1). (g) MRI of the nucleus pulposus at different grades of degeneration. (h) Western blot analysis of COL2A1, ACAN, COL1A1, MMP13, MMP9, MMP2, ADAMTS4, and ADAMTS2 expression in nucleus pulposus tissues across different degeneration grades. (i) Quantitative assessment of relative protein levels of COL2A1, ACAN, COL1A1, MMP13, MMP9, MMP2, ADAMTS4, and ADAMTS2, n = 3, P < 0.001

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Fibro-NPC is intimately associated with the angiogenesis and fibrosis of the nucleus pulposus

The intervertebral disc is widely recognized as the largest avascular structure in the human body [26]. However, the progression of IDD is characterized by the onset of angiogenesis within the nucleus pulposus. To investigate the signaling interactions among distinct subtypes of NPCs, we employed the R package “CellChat” to construct a signaling network that includes ECs. Our analysis revealed that the signaling interactions between Fibro-NPCs and EF-NPCs exhibited the greatest interaction strength, as evidenced by the highest number of interactions (Fig. 3a). Our findings suggest that, in addition to Fibro-NPC, EF-NPC is the most prevalent subtype during the late stages of nucleus pulposus differentiation (Fig. 2b). This indicates that the signaling interactions between these two subtypes are critically important in the degenerative progression of intervertebral discs. A bubble plot was generated to illustrate the signaling pathways between Fibro-NPCs and each subtype of nucleus pulposus cells (Fig. 3b). The results indicate that Fibro-NPCs engage in extensive communication with each subtype through the collagen pathway, suggesting a potential involvement in extracellular matrix remodeling and degradation. To further explore these interactions, we conducted a gene expression analysis of the collagen pathway across each subtype of nucleus pulposus cells (Fig. 3c). This analysis revealed that COL9A2 and COL9A3 were absent in the Fibro-NPC subtype, while COL4A1 and COL4A2 were uniquely expressed in this subtype [27]. The proximity of COL4A1 and COL4A2, coupled with their established roles as core genes in IDD, provides compelling evidence for their involvement in the degenerative process [28]. The high expression of COL1A1 in Fibro-NPC indicates that it may serve as a key marker of nucleus pulposus fibrosis. Additionally, our analysis shows that the FN1 and PTN-SDC4 signaling pathways are exclusive to Fibro-NPC and EF-NPC (Fig. 3b). The FN1 pathway appears to be closely linked to nucleus pulposus fibrosis, while PTN-SDC4 plays a critical role in maintaining extracellular matrix homeostasis [29, 30] (Fig. 3e). Moreover, we investigated the expression patterns of the THBS signaling pathway subtypes, which have been previously implicated as pathogenic in IDD [31]. The results demonstrate that the THBS pathway is expressed across all nucleus pulposus subtypes, with the highest level of expression observed in Fibro-NPC (Fig. 3d). Subsequently, we performed a comprehensive mapping of the signaling inflow and outflow among the various subtypes of nucleus pulposus cells, revealing a distinctive signaling profile for Fibro-NPC (Fig. 3f). A bubble diagram was constructed to illustrate the signaling pathways associated with ECs, which were then mapped to each subtype of nucleus pulposus cells. This mapping process was replicated for each subtype of nucleus pulposus cells back to the ECs (Fig. 3g). Notably, the GAS6-AXL pathway was found to be exclusive to Fibro-NPC and EF-NPC. Prior studies have indicated that the GAS pathway enhances chondrocyte survival in the context of rheumatoid arthritis [32, 33]. We hypothesize that ECs may similarly promote the survival of Fibro-NPC through the activation of the GAS pathway. Furthermore, in addition to the highly expressed VEGF signaling pathway, the unique PTN signaling pathway in Fibro-NPC plays a crucial role in facilitating endothelial cell migration [34] (Fig. 3h).

Fig. 3
figure 3

Fibro-NPC Drives Fibrosis and Angiogenesis in the Nucleus Pulposus. (a) Assessment of intercellular communication between various subtypes of NPCs and ECs in degenerated nucleus pulposus tissue. (b) Dot plot illustrating the communication probability of specific ligand-receptor pairs between Fibro-NPCs (signal senders) and various subtypes of nucleus pulposus cells (signal receivers). c-e. Genes involved in Collagen, THBS, PTN signaling network and their relative expression levels in each NP subtype. f. Distinct signaling patterns observed among various subtypes of NPCs regarding both outgoing and incoming signals. g-h. Dot plot illustrating the communication probability of specific ligand-receptor pairs between NPCs (signal senders or signal receivers) and ECs (signal receivers or signal receivers)

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Fibro-NPC promotes inflammatory infiltration in the nucleus pulposus

We applied the previously described dimensionality reduction approach to the immune cell clusters, followed by classification using the R package “SingleR”. This analysis identified four immune cell types: “T cells,” “B cells,” “M1 macrophages,” and “M2 macrophages” (Fig. 4a). Communication analysis between Fibro-NPC and each immune cell type indicated that interactions with macrophages displayed the highest intensity and frequency (Fig. 4b). Mapping signaling pathways between Fibro-NPC and ICs revealed that Fibro-NPC prominently transmits the MIF signal, with macrophages demonstrating the strongest reception (Fig. 4c-e). The MIF pathway plays a crucial role in inflammatory processes by promoting macrophage migration and recruitment and sustaining their pro-inflammatory functions [35, 36]. The SPP1 pathway was identified as an exclusive signaling source within macrophages (Fig. 4e). Notably, CD44 was identified as the primary receptor for SPP1 signals in Fibro-NPC (Fig. 4f). Prior research [37, 38] suggests that the SPP1-CD44 signaling axis may be closely related to fibrosis within the nucleus pulposus. The signaling outputs from M1 macrophages are primarily associated with inflammatory processes, while those from M2 macrophages are predominantly linked to fibrotic responses.

Fig. 4
figure 4

Fibro-NPC Promotes Inflammatory Infiltration in the Nucleus Pulposus. (a) After UMAP dimensionality reduction, immune cells are classified into four primary categories: T cells, B cells, M1 macrophages, and M2 macrophages. (b) Assessment of intercellular communication between Fibro-NPCs and ICs in degenerated nucleus pulposus tissue. (c) Dot plot illustrating the communication probability of specific ligand-receptor pairs between Fibro-NPCs (signal senders) and ICs (signal receivers). (d) Genes involved in MIF signaling network and their relative expression levels in each cell type. (e) Dot plot illustrating the communication probability of specific ligand-receptor pairs between ICs (signal senders) and Fibro-NPCs (signal receivers). (f) Genes involved in SPP1 signaling network and their relative expression levels in each cell type

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Mass spectrometry

To assess the differences in protein expression between normal and degenerated nucleus pulposus tissue, we conducted data-independent acquisition (DIA) quantitative proteomics on ten samples of normal human nucleus pulposus tissue and ten samples of degenerated tissue (Fig. 5a). The top 20 differentially expressed proteins were visualized using heatmap and volcano plot analyses, revealing 96 proteins that were upregulated and 610 that were downregulated (Fig. 5b and c). Subsequently, we performed a weighted gene co-expression network analysis (WGCNA) on all detected proteins, resulting in the identification of six distinct modules. The turquoise module demonstrated the strongest correlation with intervertebral disc degeneration (Fig. 5d-g). We identified a total of 618 intersection proteins between the differentially expressed proteins and the turquoise module from the WGCNA (Fig. 5h). Reactome enrichment analysis of these intersection proteins highlighted their involvement in several processes related to cellular aging, including protein translation, synthesis, and processing; extracellular matrix remodeling and degradation; inflammatory responses; and cell cycle regulation (Fig. 5i).

Fig. 5
figure 5

Mass Spectrometry. (a) The process of DIA quantification proteomics. (b) This heatmap displays the top 20 proteins with differential expression, ranked by adjusted p-value from smallest to largest. (c) This figure illustrates proteins demonstrating significantly different expression levels between the degenerative group and the control group (|Log2FC| >1, adjusted p-value < 0.05). (d) The WGCNA clustering graph of the sample under analysis. (e) Construct a soft thresholding power plot. f-g. IDD was strongly linked to the turquoise module. h. Venn diagram illustrating the overlap between the WGCNA turquoise module and the differentially expressed proteins. i. Reactome enrichment analysis of 618 intersecting proteins

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Machine learning identifies key proteins driving fibro-NPC development

Lasso regression analysis was performed to identify core genes associated with intervertebral disc degeneration, using the expression levels of intersection proteins as the basis for this analysis (Fig. 6a and b). Seven hub proteins were identified, with Q6FHJ7 (SFRP4) emerging as the most critical factor in intervertebral disc degeneration (Fig. 6c). A receiver operating characteristic (ROC) model was constructed to assess the model’s accuracy, revealing that the area under the curve (AUC) values for all seven hub proteins exceeded 0.9 (Fig. 6d). Notably, SFRP4 was significantly expressed in Fibro-NPC, indicating its secretion from this pathogenic subtype (Fig. 6e). In our temporal analysis, SFRP4 was also highly expressed at the terminal stage of nucleus pulposus cell differentiation (Fig. 6f). Additionally, Western blot analysis of nucleus pulposus tissue from patients with Pfirrmann grades 2, 3, and 4 demonstrated a gradual increase in SFRP4 expression associated with disc degeneration (Fig. 6g). Furthermore, the expression of SFRP4 in degenerated human nucleus pulposus tissue was significantly higher than in normal human tissue (Fig. 6h). These findings confirm that SFRP4, secreted by Fibro-NPC, is a critical protein in the pathogenesis of Fibro-NPC.

Fig. 6
figure 6

Machine Learning Identifies Key Proteins Driving Fibro-NPC Development. (a) LASSO coefficient profiles of the seven proteins in IDD (b) The log (lambda) sequence was used to construct a coefficient profile diagram. The LASSO model’s optimal parameter (lambda) was chosen. (c) The LASSO regression coefficients for the six proteins. (d) ROC assays for seven proteins. (e) Dual volcano plot illustrating the differential expression of proteins from Fibro-NPCs and mass spectrometry analysis (|Log2FC| >1, adjusted p-value < 0.01). (f) Pseudotime kinetics of SFRP4 in each subtype of NPCs. (g) Western blot analysis of SFRP4 expression in nucleus pulposus tissues across different degeneration grades. (h) Immunohistochemical analysis of SFRP4 expression in nucleus pulposus. (i) Quantitative assessment of relative protein levels of SFRP4, n = 3, P < 0.001. (j) Immunohistochemical score of SFRP4 in different degenerating tissues. n = 3, P < 0.001

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SFRP4 inhibits the canonical wnt pathway to accelerate NPCs senescence

GSEA analysis further indicated that degenerated NP tissue is closely associated with the cell cycle and the canonical Wnt pathway (Fig. 7a and b). Western blot analysis of degenerated NPCs at Pfirrmann grades 2 and 4 revealed a significant correlation between SFRP4 and aging markers P53, P21, and P16 (Fig. 7c). Four groups of nucleus pulposus cells were subsequently subjected to distinct treatments, revealing that downregulation of SFRP4 activated the GSK3β-β-catenin pathway, thereby delaying the aging process of these cells (Fig. 7d). Following the same treatment methodology, β-galactosidase staining demonstrated that SFRP4 downregulation resulted in delayed aging of the nucleus pulposus cells (Fig. 7e). Furthermore, supplementation with rhSFRP4 inhibited the classical Wnt pathway and promoted the expression of aging markers P53, P21, and P16, as confirmed by Western blot analysis (Fig. 7f). Our findings show that knockdown of SFRP4 led to downregulation, while supplementation with rhSFRP4 resulted in upregulation of the Fibro-NPC marker COL1A1, as indicated by immunofluorescence (IF) analysis. Conversely, β-catenin expression exhibited an opposing trend, highlighting the critical role of SFRP4 in the development of Fibro-NPC (Fig. 7g). Additionally, immunofluorescence analysis of ACAN and MMP2, two key indicators of nucleus pulposus degeneration, showed that ACAN was upregulated and MMP2 downregulated following SFRP4 knockdown, while the opposite results were observed following SFRP4 supplementation (Fig. 7h).

Fig. 7
figure 7

SFRP4 Inhibits the Canonical Wnt Pathway to Accelerate NPCs Senescence. a-b. GSEA of mass spectrometry. c. Western blot analysis of SFRP4, Tumor protein P53 (P53), Cyclin-Dependent Kinase Inhibitor 1 A (P21), Cyclin-Dependent Kinase Inhibitor 2 A (P16) expression in nucleus pulposus tissues across G2 and G4. d. Western blot analysis of SFRP4, Glycogen Synthase Kinase 3 Beta (GSK3β), Phosphorylated Glycogen Synthase Kinase 3 Beta (P-GSK3β), β-catenin, P53, P21, P16 after different treatments of NPCs. e. β-Galactosidase staining was performed on NPCs subjected to various treatments. f. Western blot analysis of GSK3β, P-GSK3β, β-catenin, P53, P21, P16 after different treatments of NPCs. g. Double-immunofluorescence staining of COL1A1 and β-catenin. h. Double-immunofluorescence staining of ACAN and MMP2. i. Quantitative assessment of relative protein levels of SFRP4, P53, P21, P16, n = 3, P < 0.001. j. Quantitative assessment of relative protein levels of SFRP4, GSK3β, P-GSK3β, β-catenin, P53, P21, P16, n = 3, P < 0.001. K. β-Gal positive cells after different treatment. n = 3, P < 0.001. L. Quantitative assessment of relative protein levels of GSK3β, P-GSK3β, β-catenin, P53, P21, P16, n = 3, P < 0.001. M. Quantitative analysis of COL1A1 and β-catenin fluorescence intensity. n = 3, P < 0.001. N. Quantitative analysis of ACAN and MMP2 fluorescence intensity. n = 3, P < 0.001

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Discussion

To date, low back pain associated with IDD remains a challenging condition to manage in clinical practice. Available treatment options are limited and often yield modest outcomes, placing a significant burden on the healthcare system. Investigating the molecular mechanisms underlying IDD is essential. This study reveals that Fibro-NPCs are predominantly localized in the later stages of nucleus pulposus cell differentiation, as determined by single-cell RNA sequencing. Furthermore, Fibro-NPCs were identified as the primary cell subtype implicated in IDD. To elucidate the pathogenic mechanisms underlying Fibro-NPCs, we constructed a signaling network among various subtypes of NPCs, endothelial cells ECs, and ICs using CellChat. Our findings demonstrate that Fibro-NPCs possess a unique signaling profile distinct from other nucleus pulposus cell subtypes. We suggest that Fibro-NPCs contribute to nucleus pulposus fibrosis, closely linked to the signaling output of the FN1 and collagen pathways [39]. Additionally, our findings align with previous research identifying the THBS signaling pathway as a critical pathogenic mechanism in IDD [31]. Regarding angiogenesis in the nucleus pulposus, our results indicate that the VEGF signaling pathway, characterized by significant signal exchange between Fibro-NPCs and ECs, is a common feature among all nucleus pulposus cell subtypes. Notably, the PTN signaling pathway was found exclusively in Fibro-NPCs and ECs, with PTN playing a critical role in endothelial cell migration. These findings suggest that the primary function of Fibro-NPCs in nucleus pulposus angiogenesis may be to facilitate endothelial cell migration, thereby promoting pathological angiogenesis [34, 40]. Additionally, COL4A1 and COL4A2, expressed specifically by Fibro-NPCs, are significant contributors to angiogenesis [41]. The MIF signaling pathway exhibited the strongest association with the interaction between Fibro-NPCs and M1 macrophages, playing a crucial role in macrophage recruitment [42]. Conversely, the SPP1 signaling pathway, secreted by macrophages, is significant in nucleus pulposus fibrosis [43]. Thus, Fibro-NPCs can be considered a pathogenic subtype of nucleus pulposus cells that promote IDD through mechanisms involving fibrosis, angiogenesis, and inflammatory infiltration. To elucidate the specific mechanisms underlying Fibro-NPC formation, we conducted label-free quantitative proteomics analysis comparing degenerated and normal nucleus pulposus tissues. Utilizing machine learning methods, we identified SFRP4 as a crucial factor in IDD, with significant expression levels in Fibro-NPCs. Through Reactome and GO enrichment analyses, we established that SFRP4 is integral to regulating the cell cycle in nucleus pulposus cells and is closely linked to the canonical WNT pathway. We propose that SFRP4, secreted by Fibro-NPCs, induces cell cycle arrest in nucleus pulposus cells, thereby promoting cellular senescence and facilitating progression to an advanced degenerative stage through inhibition of the canonical WNT pathway. This perpetuates a vicious cycle that progressively increases the proportion of Fibro-NPCs, contributing to the onset of IDD. To test this hypothesis, we performed SFRP4 knockdown following the induction of nucleus pulposus cell senescence with IL-1β. This intervention resulted in the activation of the WNT pathway and significant downregulation of the senescence markers P53, P21, and P16. Our findings further demonstrated that administration of rhSFRP4 inhibited the WNT pathway and upregulated senescence markers, supporting our initial hypothesis. Additionally, immunofluorescence staining revealed a significant reduction in COL1A1 expression, a marker of Fibro-NPCs, in nucleus pulposus cells following SFRP4 knockdown, while rhSFRP4 supplementation led to increased COL1A1 expression. These results suggest that SFRP4 plays a crucial role in the differentiation of Fibro-NPCs.

Conclusions

Fibro-NPCs represent a pathogenic subtype of NPCs that significantly contribute to nucleus pulposus fibrosis, angiogenesis, and inflammatory infiltration. SFRP4 is critical in the formation of Fibro-NPCs. Consequently, further research is essential to elucidate the distinct pathogenesis of Fibro-NPCs compared to other subtypes and to assess the potential of SFRP4 as a therapeutic target for IDD.

Data availability

The single-cell sequencing datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: GSE233666, GSE244889 (GEO). Other data is not publicly available due to ethical considerations and can be provided upon reasonable request, subject to approval from the Ethics Committee of Nanjing Drum Tower Hospital the Affiliated Hospital of Medical School of Nanjing university.

Abbreviations

IDD:

Intervertebral Disc Degeneration

NPCs:

Nucleus pulposus cells

Fibro-NPC:

Fibrosis-associated NPC

IC:

Immune cell

EC:

Endothelial cell

SASP:

Senescence-associated secretory phenotype

WGCNA:

Weighted gene co-expression network analysis

LASSO:

Least absolute shrinkage and selection operator

GEO:

Gene Expression Omnibus

UMAP:

Uniform Manifold Approximation and Projection

DIA:

Data Independent Acquisition

DDA:

Data Dependent Acquisition

ROC:

Receiver operating characteristic

AUC:

Area under the curve

CI:

Confidence interval

GSEA:

Gene set enrichment analysis

GO:

Gene Ontology

MRI:

Magnetic resonance imaging

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Acknowledgements

We used BioRender to create the mechanism diagram. Created in BioRender. Xu, P. (2025) (https://BioRender.com/o32a400).

Funding

The work was supported by National Natural Science Foundation of China (No. 82202711) and Jiangsu Funding Program for Excellent Post-doctoral Talent (No. 2022ZB692).

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Author notes

  1. Yili Xu, Zuozhi Xie, Shubo Gu contributed equally to this paper.

Authors and Affiliations

  1. Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Clinical College of Nanjing University of Chinese Medicine, Nanjing, 210008, China

    Yili Xu, Jinfeng Wang & Jun Jiang

  2. Division of Spine Surgery, Department of Orthopedic Surgery, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, 210008, China

    Zuozhi Xie, Shubo Gu, Zhengzheng Wu, Tao Xu, Zhenhua Feng, Yong Qiu, Zezhang Zhu, Yang Yu, Bo Shi & Jun Jiang

  3. Department of Ultrasound, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, 21008, China

    Di Zhao

  4. Department of Surgery of Spine and Spinal Cord, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, People’s Hospital of Henan University, Henan, 453003, Zhengzhou, China

    Xiang Zhao

  5. School of Clinical Medicine, Bengbu Medical University, Bengbu, 233000, Anhui, China

    Ruxue Xu

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Contributions

Jun Jiang, Bo Shi, Di Zhao, Zhenhua Feng, Tao Xu, Xiang Zhao contributed to the study conceptualization and design. Jun Jiang, Bo Shi, Zezhang Zhu, Yong Qiu, Yang Yu provided funding. Yili Xu, Zuozhi Xie, and Shubo Gu were engaged in the analysis of single-cell data. Yili Xu, Zuozhi Xie, Zhengzheng Wu, Jinfeng Wang, and Ruxue Xu were involved in the construction of machine learning models. Yili Xu, Jun Jiang interpreted the results and wrote the initial version of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Di Zhao, Bo Shi or Jun Jiang.

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Ethics approval and consent to participate

The use of patient tissue samples was approved by the Ethics Committee of Nanjing Drum Tower Hospital the Affiliated Hospital of Medical School of Nanjing university (2023-506-01). This study adhered to the principles of the Declaration of Helsinki. Informed consent was obtained from all participating patients, and tissue samples were collected in accordance with standard procedures. No remuneration was provided to the patients involved in this study.

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Xu, Y., Xie, Z., Gu, S. et al. Fibro-NPC: a pathogenic subtype identified at single-cell resolution with secreted SFRP4 as a biomarker in intervertebral disc degeneration. J Transl Med 23, 867 (2025). https://doi.org/10.1186/s12967-025-06798-4

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Journal of Translational Medicine

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