Conditional ablation of neurones in transgenic mice
Bibliographic record
Abstract
Conditional targeted ablation of specific cell populations in living transgenic animals is a very powerful strategy to determine cell functions in vivo. This approach would be of particular value to study the functions of distinct neuronal populations; however, the transgene of choice for conditional cell ablation studies in mice, the herpes simplex virus thymidine kinase gene, cannot be used to ablate neurones as its principal mode of action relies on cell proliferation. Here we report that expression of the E.coli nitroreductase gene (Ntr) and metabolism of the prodrug CB1954 (5-aziridin-1-yl-2-4-dinitrobenzamide) to its cytotoxic derivative can be used to conditionally and acutely ablate specific neuronal populations in vivo. As proof of principal, we have ablated olfactory and vomeronasal receptor neurones by expressing Ntr under the control of the olfactory marker protein (OMP) gene promoter. We demonstrate that following CB1954 administration, olfactory and vomeronasal receptor neurones expressing the transgene were selectively eliminated from the olfactory epithelium (OE), and projections to the olfactory bulb (OB) were lost. The functional efficacy of cell ablation was demonstrated using a highly sensitive behavioural test to show that ablated mice had lost the olfactory ability to discriminate distinct odors and were consequently rendered anosmic. Targeted expression of Ntr to specific neuronal populations using conventional transgenes, as described here, or by "knock-in" gene targeting using embryonic stem cells may be of significant value to address the functions of distinct neuronal populations in vivo.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.002 |
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".