Development and validation of a differentiation-related signature based on single-cell RNA sequencing data of immune cells in spinal cord injury.

Background: After spinal cord injury (SCI), the native immune surveillance function of the central nervous system is activated, resulting in a substantial infiltration of immune cells into the affected tissue. While numerous studies have explored the transcriptome data following SCI and revealed certain diagnostic biomarkers, there remains a paucity of research pertaining the identification of immune subtypes and molecular markers related to the immune system post-spinal cord injury using single-cell sequencing data of immune cells. Methods: The researchers conducted an analysis of spinal cord samples obtained at three time points (3,10, and 21 days) following SCI using the GSE159638 dataset. The SCI subsets were delineated through pseudo-time analysis, and differentiation related genes were identified after principal component analysis (PCA), cell clustering, and annotation techniques. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were employed to assess the differentiation-related genes (DRGs) across different subsets. The molecular subtypes of SCI were determined using consensus clustering analysis. To further explore and validate the correlation between the molecular subtypes and the immune microenvironment, the CIBERSORT algorithm was employed. High-value diagnostic gene markers were identified using LASSO regression, and their diagnostic sensitivity was assessed using receiver operating characteristic curves (ROC) and quantitative real-time polymerase chain reaction (qRT-PCR). Results: Three SCI subsets were obtained, and differentiation-related genes were characterized. Within these subsets, two distinct molecular subtypes, namely C1 and C2, were identified. These subtypes demonstrated significant variations in terms of immune cell infiltration levels and the expression of immune checkpoint genes. Through further analysis, three candidate biomarkers (C1qa, Lgals3 and Cd63) were identified and subsequently validated. Conclusions: Our study revealed a diverse immune microenvironment in SCI samples, highlighting the potential significance of C1qa, Lgals3 and Cd63 as immune biomarkers for diagnosing SCI. Moreover, the identification of immune checkpoints corresponding to the two molecular subtypes suggests their potential as targets for immunotherapy to enhance SCI repair in future interventions.