Tumor heterogeneity of glioblastoma analyzed via SpatialOMx and HiPLEX-IHC MALDI Imaging

Importance of the Topic

Glioblastoma is the most aggressive primary brain tumor and exhibits high intratumoral heterogeneity and a complex microenvironment that drives therapy resistance and rapid recurrence. Spatially resolved lipidomics and multiplexed protein imaging are essential to understand metabolic and cellular interactions within these tumors.

Objectives and Study Overview

This study presents a SpatialOMx workflow combining 4D LC-MS/MS lipidomics with high-resolution MALDI imaging and HiPLEX-IHC on the same tissue sections. The goal was to spatially map lipid species and correlate them with cell-type markers in glioblastoma and astrocytoma samples.

Methodology and Instrumentation

• Samples: Fresh frozen sections from three glioblastoma and one astrocytoma patient.
• 4D LC-MS/MS lipidomics: Lipid extraction via MTBE phase separation and analysis on timsTOF fleX with PASEF.
• MALDI-2 imaging: Raster scans at 20 μm resolution with ion mobility separation (TIMS) on the timsTOF fleX.
• MALDI HiPLEX-IHC: AmberGen protocol with antibodies for GFAP, Vimentin, Ki67, CD68, CD4, Collagen 1A1.
• Data processing: MetaboScape for lipid annotation using m/z and collisional cross sections; SCiLS Lab for image integration.

Key Results and Discussion

• Approximately 300 lipid species annotated; phosphatidylcholines (PC 32:1, PC 34:1) were upregulated in glioblastoma, while sphingomyelins showed lower intensities compared to astrocytoma.
• Spatial heterogeneity of lipids was evident, with distinct distributions in tumor versus microenvironment regions.
• HiPLEX-IHC revealed diverse cell populations: GFAP-positive tumor cells, CD68-positive macrophages, CD4 T cells and unexpected B-cell presence in a glioblastoma sample.
• Co-localization analyses highlighted cell-type specific lipid signatures, suggesting roles in tumor progression, immune interactions, and therapy response.

Benefits and Practical Applications

• The integrated workflow enhances MALDI lipid annotation and spatial mapping at cellular resolution.
• Enables correlation of metabolic alterations with specific cell populations in the tumor microenvironment.
• Provides a platform for identifying spatial biomarkers and metabolic targets for glioblastoma diagnosis and treatment monitoring.

Future Trends and Opportunities

Future developments may include higher plexity imaging, integration with spatial transcriptomics and proteomics, and application of machine learning for automated pattern recognition. Expanding to clinical cohorts could validate lipid-cell signatures as prognostic or predictive biomarkers.

Conclusion

This SpatialOMx approach, combining 4D lipidomics with MALDI HiPLEX-IHC, overcomes bulk analysis limitations by delivering spatially and cell-type resolved molecular maps. It offers new insights into glioblastoma heterogeneity and lays groundwork for precision diagnostics and targeted therapies.

References

1. Yagnik G et al., J Am Soc Mass Spectrom. 2021 Apr 7;32(4):977-988.
2. Saito RF et al., Front Immunol. 2022 Feb 15;13:768606.
3. Khairunnisa AR et al., Metabolites. 2022 Dec 16;12(12):1280.