3.1 Key Cell Types Involved in Glioma Progression Identified via snRNA Sequencing
Single-nucleus RNA sequencing (snRNA-seq) was conducted on tumor samples from nine glioma patients to investigate the cellular diversity within these specimens. After quality control and filtering, we analyzed 22,392 cells through dimensionality reduction clustering, identifying 22 clusters corresponding to six different cell types: oligodendrocytes (771), glial neuronal cells (13,760), astrocytes (4,980), smooth muscle cells (106), vascular cells (519), and myeloid cells (2,256) (Fig. 1A, upper). Among the 22,392 cells analyzed, 14,611 exhibited the IDH1 mutation, while 20,931 were IDH1 wild-type (Fig. 1A). UMAP plots illustrated the distribution of these six cell types across various cell cycle phases: 5,051 cells in the S-phase, 13,728 in the G1 phase, and 3,613 in the G2M phase (Fig. 1A, bottom).
A bar chart was employed to depict the distribution of the six cell types within a cohort consisting of eight patients with IDH1 wild-type lesions and one patient with IDH1 mutant lesions (Fig. 1B). This chart highlights the heterogeneity of cell types among the glioma patients. Box plots further emphasized the distinct variations among these six cellular phenotypes across the different cohorts (Fig. 1C). Marker gene expression varied among the six cell types identified in the tumor samples, with the top 10 markers for each cell type displayed in bubble plots (Fig. 1D).
RNA characteristics, including nCount_RNA, nFeature_RNA, G2M score, and S score, were visualized through UMAP plots (Fig. 1E). Word clouds displayed Gene Ontology Biological Process (GO-BP) enriched pathway terms across the cell types while volcano plots highlighted distinct genes among the six cell types (Fig. 1F). Heatmaps presented GO-BP enrichment analysis results for uniquely expressed genes within the six cellular phenotypes (Fig. 1G).
3.2 Intracellular Heterogeneity in Glia-Neuronal Cells
To analyze glia-neuronal cells for malignancy, we applied an inferred CNV algorithm to identify malignant cells at the single-cell level. Based on inferred copy number variations (CNV), cells with elevated CNV levels were classified as glioma cells (Supplementary Fig. 1) revealing three subpopulations: C0 MALAT1 + glioma cells (2,508), C1 AKAP9 + glioma cells (1,788), and C2 NUSAP1 + glioma cells (1,566) (Fig. 2A). We examined the distribution of cell cycle stages, subsets, and lineages across these three subgroups.
UMAP representations were used to visualize the CNV score, nCount_RNA, S score, G2M score, and other relevant attributes of the three cellular subgroups (Fig. 2B). Additionally, the distribution of these three subtypes was visually examined across eight cases with IDH1 wild-type lesions and one case with an IDH1 mutant lesion (Fig. 2C, left). The C2 NUSAP + glioma subpopulation was predominantly found in patients with SF12264. Box plots showed varying proportions of the three subpopulations across the groups (Fig. 2C, right). Volcano plots highlighted differentially expressed genes in each of the three subpopulations (Fig. 2D). Word clouds displayed enriched Gene Ontology Biological Process (GO-BP) pathway terms within these subgroups (Fig. 2E). Marker genes with differential expression in the three subpopulations were visualized using bubble plots and heatmaps (Fig. 2F), A heatmap was generated to illustrate the results of GO-BP enrichment analysis for the distinct gene sets in the three subgroups (Fig. 2G).
3.3 Pseudotemporal Analysis of Glial and Neuronal Cell Subpopulations Using CytoTRACE and Monocle
To explore the differentiation and developmental trajectories of glioma cell subpopulations, we conducted CytoTRACE analysis (Fig. 3A), revealing a differentiation pattern from C1 to C0 and then to C2 (Fig. 3B). A bar graph illustrates the distribution of the three cell subpopulations across IDH1 mutant and IDH1 wildtype cohorts (Fig. 3C, left). Notably, the C2 NUSAP1 + glioma cell subpopulation was exclusive to the IDH1 wildtype group. This finding provides a starting point to explore the relationship between the IDH1 mutant and wildtype groups. Additional bar graphs illustrated the distribution of the three cell subpopulations across different stages of the cell cycle (Fig. 3C, right). nd to show the changes in cell percentages during different stages of trajectory differentiation (Fig. 3D). In state 1, the C0 MALAT1-expressing glioma cells were the most prevalent. In state 2, C1 AKAP9-expressing glioma cells dominated, while in state 3, C2 NUSAP1-expressing glioma cells were the most abundant..
To gain further insights into glioma development, we conducted additional trajectory analysis of the three cellular subtypes using monoclonal techniques (Fig. 3E). The three cellular subtypes displayed a continuous distribution along the pseudotemporal trajectory, eventually diverging at a branching point. The trajectory commenced in the upper right quadrant and advanced to the differentiation point designated as state 1. At this point, the lineage split: one branch continued caudally, corresponding with state 3, while the other moved leftward, aligning with state 2. Chronologically, the C1 AKAP9 + glioma cell subpopulation appears to represent the early stage of tumorigenesis. As tumorigenesis progresses, this subpopulation differentiates into other subtypes, eventually becoming C0 DOCK5 + or C2 NUSAP + glioma cells. We identified the genes associated with the three cellular subpopulations and mapped their dynamic trajectories using pseudo-time series scatter plots, UMAP plots, and pseudo-scatter plots. The analysis revealed that the C1 cell subset, marked by the AKAP9 gene, was predominantly present early in the temporal sequence, while the C2 subset, characterized by the NUSAP1 gene, was more prevalent towards the end of the pseudotemporal series (Fig. 3F).
3.4 Slingshot Analysis of Pseudotemporal Trajectories of Glioma Cell Subpopulations
We further analyzed the differentiation trajectories of the three glioma cell subpopulations (C0 to C2) using the slingshot method. These subpopulations were consistently distributed along the temporal axis, forming a lineage (Fig. 4A). Spectrum 1 illustrates the progression from C1 to C0, followed by a shift to C2 (Fig. 4B). To understand the biological mechanisms underlying these pseudotemporal trajectories, We conducted enrichment analysis for GO-BP. The C1 subpopulation was associated with processes like polymerization and microtubule dynamics. C2 was linked to signaling mediation and immune responses, while C3 was connected to pyrimidine metabolism, and C4 to mitotic processes (Fig. 4C). Scatter plots illustrated the distribution of different cell subpopulations along Spectrum 1, highlighting their unique differentiation patterns over pseudotime (Fig. 4D).
3.5 CellChat Analysis of Cell-Cell Interactions and PTN Signaling Pathway Visualization
To better understand complex cellular responses, we aimed to explore intercellular dynamics and ligand-receptor communication networks. Using CellChat analysis, we constructed intercellular communication networks involving various cell types, such as glioma subpopulations, oligodendrocytes, myeloid cells, astrocytes, smooth muscle cells, and vascular cells. We quantified interaction frequencies by measuring connection thickness and assessed interaction intensities based on line weights (Fig. 5A). To investigate the coordination of various cell populations and signaling pathways, we used CellChat's non-negative matrix decomposition technique for pattern recognition. Our analysis revealed communication patterns connecting cell populations as either signal transmitters (outbound) or recipients (inbound). Using CellChat's gene expression tool, we further explored these cellular interactions and signaling pathways. We first linked hypothesized communication patterns to secretory cell cohorts to clarify efferent signaling modalities. Then, we correlated these patterns with secretory cell populations. We identified three communication modes, each associated with a specific cell type: mode 1 (glioma subpopulations), mode 2 (vascular endothelial cells), and mode 3 (oligodendrocytes and myeloid cells) (Fig. 5B). For glioma efferent signaling, mode 1 was characterized by pathways such as PTN and NCAM. In contrast, glioma afferent signaling mainly involved mode 1 communication patterns, including pathways like VEGF, PTN, and JAM (Fig. 5D). Employing CellChat's pattern recognition methodology, we quantitatively analyzed the ligand-receptor interactions within gliomas to pinpoint key signaling pathways associated with the three cell subtypes. In gliomas, each cell variant functions as both a sender and receiver in the communication network, releasing various cytokines or ligands and responding to signals from other cells (Fig. 5C).
We created a heatmap to visualize the intensity of afferent and efferent signals across all cell interactions, specifically focusing on participation in the PTN signaling pathway (Fig. 5E). Utilizing a centrality measurement algorithm, we classified cell types according to their roles as mediators and influencers in intercellular communication. The Glioma C2 subset, characterized by NUSAP expression, was identified as a pivotal player in the PTN signaling cascade. Additionally, we found that the NCAM signaling pathway, involved in cell adhesion, and the VEGF pathway, associated with angiogenesis, were particularly prominent in the C1 AKAP9 + Glioma subpopulation (Fig. 5F). Violin plots highlight cellular interactions, showing the NUSAP + Glioma subpopulation in the C2 group with elevated activity in the PTN signaling cascade. In contrast, the AKAP9 + Glioma subset in the C1 group was notably involved in the NCAM and VEGF signaling pathways (Fig. 5G). We identified all eight cell types within glioma tissues as origins of the PTN signaling cascade. The three glioma subtypes, along with other cell types, were considered potential targets, highlighting their correlations within the PTN signaling pathway in a hierarchical plot. The findings suggest that, except for myeloid cells, oligodendrocytes, and vascular endothelial cells (VECs), various cell types act as signaling mediators within the PTN cascade. A heatmap displaying the complex network of cell-cell interactions is shown in Fig. 5I.
3.6 Development and Validation of a Prognostic Model
To assess the clinical significance of the identified cell types, we performed a univariate Cox analysis on the top 100 marker genes within the C2 NUSAP + Glioma subgroup. This analysis revealed three genes—RPA3, TUBA1C, and NUDT1—that are associated with patient outcomes (Fig. 6A). To address multicollinearity within the gene pool, we used lasso regression to refine the selection, identifying three key genes crucial to the NUSAP + Glioma scoring system. Lambda plots validated the robustness of this gene subset (Fig. 6B). Patients were categorized into two groups based on their expression levels of the selected genes: high and low NUSAP + Glioma score groups. Survival analysis indicated that individuals in the low NUSAP + Glioma score group experienced superior survival outcomes compared to those in the high NUSAP + Glioma score group (Fig. 6C). Survival analysis was conducted for the three genes that make up the NUSAP + Glioma score model (Fig. 6D). The results consistently demonstrated that elevated expression levels correlated with poorer survival, whereas reduced expression levels were associated with improved prognostic outcomes, thereby affirming their role as risk factors. The NUSAP + Glioma score for each patient in the TCGA-GBM cohort was determined by integrating gene expression levels with their corresponding regression coefficients. Subsequently, patients were categorized into high and low score groups based on the median value. A higher NUSAP + Glioma score was associated with reduced survival times. Expression levels of the three genes in the model are depicted in Fig. 6E. Correlation analysis revealed an inverse relationship between overall survival (OS) and both the NUSAP + Glioma score and the three genes. These relationships are visually represented in scatter plots (Fig. 6F). ROC curves were utilized to evaluate the predictive accuracy of the NUSAP + Glioma score for 1-year, 3-year, and 5-year survival, yielding AUC values of 0.579, 0.792, and 0.625, respectively (Fig. 6G). Scatter plots displayed the genetic factors correlated with NUSAP + Glioma scores (Fig. 6H), and Fig. 6I highlighted differences in gene expression levels between high and low NUSAP + Glioma score groups. Multifactorial Cox regression analysis was performed to determine if the NUSAP + Glioma score serves as an independent prognostic factor. This analysis incorporated variables such as age, race, T stage, N stage, M stage, and the NUSAP + Glioma score, revealing the latter as a significant independent predictor of prognosis in glioma patients (p < 0.05) (Fig. 6K). Additionally, a column chart was generated to integrate clinical and pathological risk factors with cell type characteristics, utilizing age, race, and T stage. This chart provides an effective prediction of patient survival probabilities at 1, 3, and 5 years (Fig. 6J).
3.7 Immune Infiltration Differences between High and Low NUSAP + Glioma Score Groups
To investigate immune infiltration in gliomas and assess differences in immune cell populations between high and low NUSAP + Glioma score groups, we utilized heatmaps to visualize the variations in immune cell infiltration within each group (Fig. 7A). We next evaluated immune cell infiltration in glioma patients by analyzing data from the TCGA repository using the CIBERSORT computational tool. Heatmaps were employed to display the distribution of 22 distinct immune cell types identified in the samples (Fig. 7B). Bar graphs were used to depict the relationships between immune cell types and glioma subpopulation markers. The NUSAP + Glioma scores showed a positive correlation with M0 macrophages, resting dendritic cells, and other immune cells, whereas they were negatively correlated with activated NK cells, monocytes, and additional cell types (Fig. 7C). Various methods of immune cell content assessment were used to compare and summarize the associations between the three genes under study and immune cells. These relationships were illustrated using heatmaps, where positive correlations were represented in red and negative correlations were depicted in blue (Fig. 7D). Violin plots demonstrated immune dysfunction and tumor rejection across both high and low scoring groups, with a significantly elevated TIDE score observed in the low scoring group compared to the high scoring group (Fig. 7E). The expression of ADORA2A was present in tumors from both groups, but it was notably lower in the high NUSAP + Glioma score group relative to the low scoring group.
3.8 Differential and Enrichment Analysis
To compare the high and low NUSAP + Glioma score groups, we employed volcano plots and heat maps to illustrate the expression patterns of distinct genes in each group (Fig. 8A, B). To investigate the role of the C2 NUSAP + Glioma subgroup in glioma pathogenesis, we conducted functional enrichment analysis on genes that distinguish these groups. Bubble plots presented the results of Gene Ontology (GO) enrichment analysis, highlighting that these genes are primarily involved in oligosaccharide binding, peptidoglycan binding, and pathways associated with auditory receptor cell development (Fig. 8D). KEGG enrichment analysis, depicted in bar graphs, revealed significant associations with pathways such as neuroactive ligand-receptor interaction and the cAMP signaling pathway (Fig. 8C). Additionally, Gene Set Enrichment Analysis (GSEA) scores indicated gene enrichment across various pathways, as shown in GO-BP-enriched entries for differentially expressed genes (Fig. 8E).
3.9 Mutation analysis
We analyzed and visualized gene mutations within the tumor microenvironment (TME) to determine their correlation with immune components across two cell cohorts. This analysis revealed differences in the top 30 genes with the highest mutation frequencies within these mesenchymal cell cohorts. The vertical bar shows the mutation burden per sample, while the horizontal bar indicates the overall mutation prevalence of each gene across the samples (Fig. 8F). No significant chromosomal copy number variations (CNVs) were detected in the genes analyzed, as shown by the lack of significant gains or losses in the CNV profile (Fig. 8G). Violin plots were employed to examine mutation burden variations between high and low NUSAP + Glioma score groups, revealing no statistically significant differences (Fig. 8H). However, scatter plots showed a statistically significant correlation (P < 0.05) between mutation load and NUSAP + Glioma scores (Fig. 8I). Tumors were classified into four categories based on mutation burden: high NUSAP + Glioma score with high TMB, high NUSAP + Glioma score with low TMB, low NUSAP + Glioma score with high TMB, and low NUSAP + Glioma score with low TMB. Survival analysis revealed that the group with a low NUSAP + Glioma score and high TMB exhibited the most favorable survival outcomes, whereas the group with a high NUSAP + Glioma score and low TMB showed the poorest prognosis (Fig. 8J).
3.10 Drug sensitivity analysis
Violin plots were utilized to depict the differential responses to various medications between high and low NUSAP + Glioma score groups, emphasizing variations in drug sensitivity (Fig. 8K). The IC50 value for Axitinib was elevated in the high NUSAP + Glioma score group relative to the low score group, indicating a decreased drug responsiveness in the former.
3.11 Knockdown of NUSAP1 Suppresses Proliferation, Migration, and Metastasis in Glioma Cells
To examine the role of NUSAP1 in glioma, we employed gene knockdown via transfection and validated its efficiency with RT-qPCR (Supplementary Fig. 2). Colony formation assays were performed on U251 and LN229 glioma cell lines, comparing a negative control (NC) group with an si-NUSAP1 experimental group (Fig. 9B). T Results demonstrated that NUSAP1 inhibition led to reduced colony size in both U251 and LN229 cells, indicating that downregulation of NUSAP1 impedes glioma cell proliferation (Fig. 9A). To evaluate NUSAP1's impact on glioma cell migration, we conducted scratch and transwell assays (Fig. 9C). These assays revealed a marked decrease in the migratory capacity of U251 and LN229 cells following NUSAP1 knockdown (Fig. 9A). Further, CCK-8 assays confirmed that NUSAP1 suppression impeded both proliferation and migration of glioma cells (Fig. 9D). To further validate NUSAP1’s regulatory role, we generated stable NUSAP1 knockdown cell lines and performed apoptosis flow cytometry assays. The data indicated a significant increase in apoptosis in glioma cells after NUSAP1 knockdown (Fig. 9E, F). Additionally, we assessed the impact of NUSAP1 knockdown on lung metastasis using tail vein injection in animal models. Results showed a notable reduction in the metastatic potential of glioma cells with NUSAP1 knockdown (Fig. 9G, H).