Spatially resolved transcriptomics and graph-based deep learning improve accuracy of routine CNS tumor diagnostics.

ABSTRACT: 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

SUBMITTER: Ritter M 

PROVIDER: S-EPMC11864981 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature