Establishing <em>In Vitro</em> Models of Dorsal Root Ganglia Culture: Complementary Approaches for Investigating Cancer-Nerve Crosstalk
Bibliographic record
Abstract
The contribution of the nervous system to the tumor microenvironment and the importance of neural invasion as a route for cancer dissemination are being increasingly recognized. Interactions of cancer cells with neurons can promote their invasion around and into nerves, a feature of many cancers with poor clinical outcomes. In vitro models to study reciprocal interactions between neurons and cancer cells provide valuable tools for understanding cancer spread and identifying approaches to mitigate it. Here, we describe a protocol for murine dorsal root ganglia (DRG) isolation and the establishment of both whole mount and dissociated monolayer cultures that can be used to visualize neuron morphology and neurite outgrowth over time. Whole DRGs mounted in Matrigel preserve nerve architecture and responses to stimuli in a heterogeneous environment more similar to the in vivo nerve, while dissociated nerve cultures allow assessment of direct cell-cell interactions more closely. Once DRG cultures are established, cancer cells can be added to generate co-cultures that can be used to visualize changes in neurite outgrowth and nerve morphology in response to cancer cells. Growth or motility of cancer cells in response to nerve-derived signals over time or under conditions of growth stimulation or inhibition can be assessed, as well as visualizing the effects of direct contact between cancer cells and nerve extensions. As both co-culture models can be generated simultaneously, this protocol provides a more comprehensive view of the impact of cancer-neuron interactions and facilitates comparisons of treatment conditions and integration of information from the cellular level and whole ganglia. This protocol will facilitate the study of nerve-tumor interactions and can be used for a wide range of applications, including studies of cell signaling, drug screening, or study of the heterogeneity of the tumor-nerve environment and the mechanisms of tumor dissemination along nerves.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".