Development and validation of an advanced ex vivo brain slice invasion assay to model glioblastoma cell invasion into the complex brain microenvironment
Bibliographic record
Abstract
Organotypic cultures of murine brain slices are well-established tools in neuroscience research, including electrophysiology studies, modeling neurodegeneration, and cancer research. Here, we present an optimized ex vivo brain slice invasion assay that models glioblastoma multiforme (GBM) cell invasion into organotypic brain slices. Using this model, human GBM spheroids can be implanted with precision onto murine brain slices and cultured ex vivo to allow tumour cell invasion into the brain tissue. Traditional top-down confocal microscopy allows for imaging of GBM cell migration along the top of the brain slice, but there is limited resolution of tumour cell invasion into the slice. Our novel imaging and quantification technique involves embedding stained brain slices into an agar block, re-sectioning the slice in the Z-direction onto slides, and then using confocal microscopy to image cellular invasion into the brain tissue. This imaging technique allows for the visualization of invasive structures beneath the spheroid that would otherwise go undetected using traditional microscopy approaches. Our ImageJ macro (BraInZ) allows for the quantification of GBM brain slice invasion in the Z-direction. Importantly, we note striking differences in the modes of motility observed when GBM cells invade into Matrigel in vitro versus into brain tissue ex vivo highlighting the importance of incorporating the brain microenvironment when studying GBM invasion. In summary, our version of the ex vivo brain slice invasion assay improves upon previously published models by more clearly differentiating between migration along the top of the brain slice versus invasion into the slice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".