Matthew D’Antuono

The paper integrates single-cell RNA sequencing data with drug response signatures to identify drug-sensitive and resistant glioblastoma cell states and predict synergistic drug combinations.

OrthologAL converts non-human transcriptomics data to human orthologs for drug discovery, preserving data structure and enabling use with human-annotated databases.

This study identifies MAT2A and AHCY as critical for glioblastoma survival by regulating antioxidant metabolism, showing their inhibition reduces cell viability and oxidative stress.

The paper identifies three single-cell vulnerability states in glioblastoma using transcriptomic data and CRISPR screens, showing differential drug sensitivity and spatial distribution.