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

• Published in: PLoS biology Vol. 22; no. 5; p. e3002640
• Main Authors: Yabo, Yahaya A, Heiland, Dieter Henrik
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30.05.2024; Public Library of Science (PLoS)
• Subjects: Algorithms; Animals; Biology and Life Sciences; Brain cancer; Brain Neoplasms - genetics; Brain Neoplasms - pathology; Brain Neoplasms - therapy; Brain research; Brain tumors; Cost analysis; Data analysis; Disease resistance; Genomics; Glioblastoma; Glioblastoma - genetics; Glioblastoma - pathology; Glioblastoma - therapy; Glioma; Growth factors; Heterogeneity; Humans; Kinases; Medical prognosis; Medical research; Medicine and Health Sciences; Metastases; Oncology; Plastic foam; Quality of life; Research and Analysis Methods; Single-Cell Analysis - methods; Stem cells; Transcriptome; Transcriptomics; Tumorigenesis; Tumors
• ISSN: 1545-7885; 1544-9173; 1545-7885
• DOI: 10.1371/journal.pbio.3002640

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.