Spatial omics in high-grade glioma: study design, analytical pitfalls, and standards for reproducible neuro-oncology

High-grade gliomas (HGGs), particularly glioblastoma (GBM), remain highly lethal despite extensive molecular profiling and attempts at therapeutic innovation. Treatment failure is driven in part by spatially organized heterogeneity, including hypercellular cores, infiltrative margins, perivascular niches, and hypoxic/perinecrotic zones that concentrate stress adaptation, immune suppression, and therapy tolerance. Spatial omics can map these programs in situ, but reported findings often diverge because pre-analytical variation, sampling bias, platform artifacts, segmentation/spot mixing, and inappropriate spatial assumptions can dominate apparent biology, especially in necrotic, hemorrhagic, and myelin-rich brain tissue. Here, we propose a reproducibility framework for spatial omics in HGGs that links compartment-aware study design, platform selection, and robust analysis and validation. We outline study designs aligned to diagnostic, mechanistic, translational, and trial-embedded questions; compare major spatial transcriptomic, proteomic, and metabolomic platforms with glioma-specific failure modes; and propose practical pipelines for QC, segmentation, deconvolution, multimodal integration, and spatial interaction modeling. Finally, we provide a glioma-tailored reporting checklist to improve comparability across multicenter and clinical translation.