P.103 Imaging neuron-glioma cell interactions in freely behaving animals with a novel implantable mini-microscope
Bibliographic record
Abstract
Background: High grade gliomas (HGG) are diffusely infiltrative brain tumours with dismal prognosis. Recent studies from our lab have demonstrated that glioma cells form synapses with surrounding neurons, and proliferate in response to neuronal input. How these neuron-glioma networks develop, and are influenced by experience, is currently unknown. We aimed to develop a novel imaging tool to study neuron-glioma cell interactions in freely behaving animals. Methods: Several patient-derived HGG cell lines were transfected to express the green calcium indicator GCaMP6s. These cells were xenografted into the premotor cortex of mice, along with a virus expressing the red calcium indicator jRGECO1a under a neuron-specific synapsin promotor to allow dual-color imaging of neurons and glioma cells. The Inscopix mini-microscope system was implanted into the cortex to allow real-time live calcium imaging in freely behaving animals. Results: Several HGG cell lines effectively expressed the GCaMP6s calcium indicator. In vivo, we were successfully able to image both neurons and glioma cells simultaneously in freely behaving mice in real time. Conclusions: The Inscopix system has been modified for studying cancer cells for the first time. This technology will be used to study how pharmacological agents and neuronal experience shape neuron-glioma circuit dynamics, to develop new therapeutic strategies for HGG.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".