Akademik Veri Yönetim Sistemi
8th The International Symposium on Health Informatics and Bioinformatics, İstanbul, Türkiye, 30 Ekim - 02 Kasım 2025, ss.1, (Özet Bildiri)
Spatial transcriptomics technologies have significantly advanced our understanding of tumor biology, but their implementation remains limited by sample type constraints and high costs. In this study, we introduce a computational strategy that enables spatial-like transcriptomic inference using region-specific bulk RNA-seq data from a human glioblastoma xenograft model in nude mice. Human U87 glioblastoma cells were orthotopically implanted into mouse brains, after which anatomically annotated sections were collected. Bulk RNA sequencing was performed separately for tumor core regions (composed of human cells) and the surrounding microenvironment (composed of murine cells). Reads were aligned to species-specific reference genomes (GRCh38 and GRCm39), and gene-level quantification was followed by one-to-one human–mouse ortholog mapping using the orthogene R package. The resulting cross-species expression matrix was subjected to normalization, batch correction, and downstream analyses including pathway enrichment, immune landscape deconvolution, and spatial-like modeling using tools such as CIBERSORTx, GSVA, and CARD. This approach enabled the dissection of distinct transcriptional programs within tumor and stromal compartments, revealing functional divergence across immune regulation, metabolic activity, and developmental pathways. Importantly, spatially-informed gene expression gradients were computationally reconstructed, mimicking the organization of tumor and peritumoral regions despite the lack of true spatial sequencing. These findings demonstrate that regionally dissected, species-aware bulk RNA-seq can serve as a powerful alternative to spatial transcriptomics, particularly in xenograft models and FFPE tissues. The proposed pipeline offers a cost-effective and scalable framework for exploring spatially organized biological phenomena across heterogeneous tissue systems.