Understanding glioblastoma at the single-cell level: Recent advances and future challenges

Abstract

Glioblastoma, the most aggressive and prevalent form of primary brain tumor, is characterized by rapid growth, diffuse infiltration, and resistance to therapies. Intrinsic heterogeneity and cellular plasticity contribute to its rapid progression under therapy; therefore, there is a need to fully understand these tumors at a single-cell level. Over the past decade, single-cell transcriptomics has enabled the molecular characterization of individual cells within glioblastomas, providing previously unattainable insights into the genetic and molecular features that drive tumorigenesis, disease progression, and therapy resistance. However, despite advances in single-cell technologies, challenges such as high costs, complex data analysis and interpretation, and difficulties in translating findings into clinical practice persist. As single-cell technologies are developed further, more insights into the cellular and molecular heterogeneity of glioblastomas are expected, which will help guide the development of personalized and effective therapies, thereby improving prognosis and quality of life for patients.

Fig 1. Complex interactions in the glioblastoma tumor microenvironment.

Illustration of cellular differentiation and dynamic adaptation, as well as interactions in the complex microenvironment of glioblastomas. AC, astrocyte; CAF, cancer-associated fibroblast; HGF, hepatocyte growth factor; IL-10, interleukin 10; JAK, Janus kinase; MES, mesenchymal; NPC, neural progenitor cell; OPC, oligodendrocyte progenitor cell; PDGF, platelet-derived growth factor; PD-L1, programmed death-ligand 1; STAT, signal transducer and activator of transcription; TAM, tumor-associated macrophage; TGFβ, transforming growth factor β.

Fig 2. Overview of the conceptual design of single-cell glioblastoma studies.

(A) Illustration of the steps in study design of single-cell experiments. (B) Illustration of the steps in single-cell analysis. DE, differential expression; ICA, independent component analysis; MNN, mutual nearest neighbor; PCA, principal component analysis; sc, single-cell; SNN, shared nearest neighbor; TSNE, t-distributed stochastic neighbor embedding; UMAP, uniform manifold approximation and projection.

Fig 3. Future challenges in single-cell profiling of glioblastoma.

The objective of modern multiomic single-cell analysis is to build predictive models to predict outcome and optimize tumor diagnostics; model genetic or environmental perturbations; or predict the response to drug treatments or screen for target therapies. This illustration provides an overview of the possibilities in AI-based modeling of cellular responses and clinical data integration. AI, artificial intelligence; CITE-seq, cellular indexing of transcriptomes and epitopes sequencing; scATAC-seq, single-cell assay for transposase-accessible chromatin with sequencing; scRNA-seq, single-cell RNA sequencing.