Research > Focus C > Work Packages C04
Summary
Functional cross-talk between metabolism, transcription factor activity and kinase signaling, which critically impacts glioblastoma progression by modulating tumor cell malignancy and anti-tumor immunity, and its pharmacological modulation will be investigated by spatial multi-omics in patient-derived organoids and vital patient samples. This project aims at integrating spatial maps obtained by spatial transcriptomics, targeted proteomic and metabolomic mass spectrometry imaging (MSI) as well as MSI-guided laser microdissection and nanoLC-MS for identification, visualization and joint interpretation of spatial molecular profiles associated with response and resistance in glioblastoma.
Task 1
Elucidation of molecular mechanisms underlying the crosstalk between metabolic and signaling pathways in glioblastoma
Steps / Workflow
- Modulation of TF activity, kinase signaling and/or metabolism in glioblastoma cells and organoids.
- Analysis of their impact on each other by RNAseq , LC MS metabolomics, phospho proteomics, single cell MSI and/or MSI 3D reconstructions.
- Data analysis and integration
- Translation to primary human glioblastoma resectates.
Task 2
Development of an interventional spatial OMICs technology combining spatial transcriptomics, targeted proteomic and metabolomic mass spectrometry imaging (MSI)
Steps / Workflow
- Treatment of glioblastoma 3D organoids and tissue in bioreactors with pharmaceuticals.
- Spatially resolved analysis of the frozen sections by spatial transcriptomics as well as MS based multi marker IHC (“spatial proteomics”), and spatial
metabolomics. - Multimodal probabilistic analysis and 3D reconstruction of serial organoid sections.
Task 3
Development of computational tools for spatial multi OMICs data integration and spatially resolved AI based data analysis
Steps / Workflow
- Co registration of datasets using the python API in M2aia software linked to DKFZ’s Molecular Imaging Tool Kit (MITK).
- Development of R/Python tools for data fusion and integration of different modalities.
- Development of R/Python tools for AI-based cell segmentation and correlative machine learning.
Previous funding period
PRINCIPLE INVESTIGATORS
HOPF, CARSTEN, PROF. DR. RER. NAT., DIPL.-BIOCHEM.
Head of CeMOS
Hochschule Mannheim, Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Paul-Wittsack-Str. 10, 68163 Mannheim, Germany
OPITZ, CHRISTIANE, DR. MED.
Division Head
Metabolic Crosstalk in Cancer
German Cancer Research Center
Im Neuenheimer Feld 280,
69120 Heidelberg, Germany