From patient to tumor organoid: Culture protocol choice controls glioblastoma tumor architecture and identity
| Authors | |
|---|---|
| Year of publication | 2026 |
| Type | Peer-reviewed scientific article |
| Magazine / Source | BRAIN PATHOLOGY |
| Citation | |
| web | https://onlinelibrary.wiley.com/doi/10.1111/bpa.70125 |
| Doi | https://doi.org/10.1111/bpa.70125 |
| Keywords | extracellular matrix; glioblastoma; proteomics; tumor microenvironment; tumor organoids |
| Description | Patient-derived tumor organoids are widely used in cancer research, yet the biological impact of tissue processing during model generation remains unclear. Fragment-based and dissociation-based (DIS) approaches are commonly assumed to trade fidelity for uniformity, but their molecular consequences remain incompletely defined. To address this gap, we performed a proteome-wide comparison of fragment-based (cut-and-culture [CUT]) and DIS glioblastoma organoid protocols using quantitative mass spectrometry. Organoids from multiple patient tumors were cultured under growth factor-free or growth factor-supplemented conditions and compared with matched primary tissue. Results show that both protocols produced technically robust glioblastoma organoids when maintained in their native media. However, CUT organoids matched the reproducibility of DIS cultures while preserving a broader extracellular matrix (ECM) repertoire and networks linked to collagen assembly, vascular support, and cell-matrix signaling. DIS cultures were biased toward exogenous basement membrane components and proliferative, growth factor-responsive states. Across tumors, CUT organoids consistently showed greater proteomic similarity to matched primary tissue, retaining neural, glial, stromal, and extracellular features largely absent from DIS models. Taken together, fragment-based glioblastoma organoids can be both reproducible and biologically faithful. Tissue dissociation acts as a major perturbation that reshapes ECM organization, cellular states, and tumor identity, making protocol choice a critical determinant of model fidelity and translational relevance. |
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