bioRxivCancer BiologyPreprint
Mapping the spatial architecture of glioblastoma from core to edge delineates niche-specific tumor cell states and intercellular interactions
Saad M. KhaniD, Anthony Z. WangiD, Rupen R. DesaiiD, Gyeong Dae KimiD, Colin R. McCornackiD, Rui Sun, Sonika M DahiyaiD, Jennifer Ann FoltziD, Ngima D. SherpaiD, Katherine XuiD, Harrison Olszewski, Markus Anzaldua-Campos, Yuhui Song, Elizabeth Seider, Natan Pirete Gadens, Jose Maldonado, Lydia Leavitt, Timothy R. WestiD, Alexander F WangiD, Bryan D. ChoiiD, Eric C. LeuthardtiD, Bhuvic PateliD, Kathleen Pfaff, Linda Nieman, Aleksandar Krbanjevic, , Allegra A. PettiiD
Abstract
Treatment resistance in glioblastoma (GBM) is largely driven by the extensive multi-level heterogeneity that typifies this disease. Despite significant progress toward elucidating GBM's genomic and transcriptional heterogeneity, a critical knowledge gap remains in defining this heterogeneity at the spatial level. To address this, we employed spatial transcriptomics to map the architecture of the GBM ecosystem. This revealed tumor cell states that are jointly defined by gene expression and spatial localization, and multicellular niches whose composition varies along the tumor core-edge axis. Ligand-receptor interaction analysis uncovered a complex network of intercellular communication, including niche- and region-specific interactions. Finally, we found that CD8+GZMK+ T cells colocalize with LYVE1+ CD163+ myeloid cells in vascular regions, suggesting a potential mechanism for immune evasion. These findings provide novel insights into the GBM tumor microenvironment, highlighting previously unrecognized patterns of spatial organization and intercellular interactions, and novel therapeutic avenues to disrupt tumor-promoting interactions and overcome immune resistance.
- organism: human
- assay: spatial transcriptomics
- research focus: cell-cell communication
- research focus: spatial domain detection
- research focus: cell type annotation
- approach: experimental assay
- approach: statistical model
- Citations
- 4
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