Spatially resolved transcriptomics and graph-based deep learning improve accuracy of routine CNS tumor diagnostics - Ritter, Michael; Blume, Christina; Tang, Yiheng; Patel, Areeba; Patel, Bhuvic; Berghaus, Natalie; Kada Benotmane, Jasim; Kückelhaus, Jan; Yabo, Yahaya; Zhang, Jiajia; Grabis, Elena; Villa, Giulia; et al. (Nature Cancer, 2025)
The diagnostic landscape of brain tumors integrates comprehensive molecular markers alongside traditional histopathological evaluation. DNA methylation and next-generation sequencing (NGS) have become a cornerstone in central nervous system (CNS) tumor classification. A limiting requirement for NGS and methylation profiling is sufficient DNA quality and quantity, which restrict its feasibility. Here we demonstrate NePSTA (neuropathology spatial transcriptomic analysis) for comprehensive morphological and molecular neuropathological diagnostics from single 5-µm tissue sections. NePSTA uses spatial transcriptomics with graph neural networks for automated histological and molecular evaluations. Trained and evaluated across 130 participants with CNS malignancies and healthy donors across four medical centers, NePSTA predicts tissue histology and methylation-based subclasses with high accuracy. We demonstrate the ability to reconstruct immunohistochemistry and genotype profiling on tissue with minimal requirements, inadequate for conventional molecular diagnostics, demonstrating the potential to enhance tumor subtype identification with implications for fast and precise diagnostic workup.
CRIS-Team Scopus Importer:2025-02-14

Understanding glioblastoma at the single-cell level: Recent advances and future challenges - Yabo, Yahaya; Heiland, Dieter Henrik (Plos Biology, 2024)
AU Glioblastoma,: Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly 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.
CRIS-Team Scopus Importer:2024-06-14