Single-cell RNA sequencing reveals the interaction between annulus fibrosus cells and macrophages and its key pathways in intervertebral disc degeneration

English Keywords:Intervertebral disc degeneration  Macrophage  Annulus fibrosus cell  Single-cell RNA sequencing  SPP1-CD44 pathway

English Abstract:

  【Abstract】 Objectives: To elucidate the spatiotemporal dynamics of immune cell-annulus fibrosus interactions through single-cell RNA sequencing(scRNA seq). Methods: Twelve 8-week-old male Sprague Dawley rats were randomly divided into three groups, normal control(NT), needle puncture-induced degeneration 7d(IDD7), and needle puncture-induced degeneration 14d(IDD14) groups. Intervertebral disc tissues from three rats per group were pooled as one scRNA-seq sample(i.e., one NT/IDD7/IDD14 sample each, n=3 rats/group). Following digestion with a mixture of collagenases without flow cytometry sorting, scRNA-seq was performed for the three samples using the BD Rhapsody platform. Cellular dynamics were analyzed via the following pipeline: Quality control, normalization, and batch-effect correction(using Harmony algorithm) of the raw count matrix were performed with Seurat, retaining mean gene expression for subsequent analysis. 2000 highly variable genes were selected using the FindVariableFeatures method. Following PCA dimensionality reduction, cells were clustered via the Louvain algorithm (resolution=0.2) and subsequently annotated using SingleR to identify marker genes for each subpopulation. Differentially expressed genes (DEGs) among the NT, IDD7, and IDD14 groups were identified(thresholds: |logFC|>0.5, P<0.05). Functional enrichment analyses for gene ontology(GO) biological processes and KEGG pathways were performed on the DEGs using ClusterProfiler and enrichR(P<0.05, gene count ≥2) to screen for significantly associated pathways. A pseudotemporal trajectory was constructed with Monocle3 by converting the integrated data into a cell_data_set object, performing UMAP dimensionality reduction and Louvain clustering, and then employing the learn_graph and order_cells functions to delineate the cell state evolution path. Cell-cell communication was analyzed using CellChat and CellPhoneDB to calculate interaction probabilities and visualize the dynamic changes of key signaling pathways(e.g., cytokines, growth factors) between NT and IDD conditions. Co-localization and quantitative expression analysis of proteins such as CD68(a macrophage marker), SPP1, and CD44 were conducted using immunofluorescence staining. Results: The scRNA-seq data identified nine major cell populations: nucleus pulposus cells(NP), annulus fibrosus cells(AF), macrophages(Mφ), monocytes, neutrophils, T cells, fibroblasts, endothelial cells, and smooth muscle cells. 74 genes were consistently differentially expressed, predominantly associated with immune cell chemotaxis. Temporal molecular changes in AF cells were systematically delineated. AF cells exhibited polarization into reparative(Fibro-1/3) and exhausted(Fibro-2) subtypes, providing potential target subpopulations for precise intervention. The T1 phase in pseudotime(corresponding to IDD7d) likely represented a critical juncture transitioning from stress-protective responses to fibrosis, offering a potential therapeutic window to prevent irreversible damage. Analysis of dynamic cellular interactions revealed that the exhausted Fibro-2 subtype engaged in the highest number of interactions with macrophages. The SPP1 signaling axis was predominantly initiated by Fibro-2 cells, with Mφ-3 as the primary recipient, suggesting this pathway may drive AF cells toward a pro-fibrotic phenotype and potentially contribute to matrix degradation and fibrotic remodeling. Conclusions: AF cells may enhance their interaction with macrophages via SPP1-CD44, activating inflammatory responses and accelerating matrix degradation. This reveals the temporal regulation of cellular interactions in intervertebral disc degeneration models and provides new targets for precise intervention in IDD.