A simple and reliable method for claustrum localization across age in mice
Bibliographic record
Abstract
The anatomical organization of the rodent claustrum remains obscure due to lack of clear borders that distinguish it from neighboring forebrain structures. Defining what constitutes the claustrum is imperative for elucidating its functions. Methods based on gene/protein expression or transgenic mice have been used to spatially outline the claustrum but often report incomplete labeling and/or lack of specificity during certain neurodevelopmental timepoints. To reliably identify claustrum projection cells in mice, we propose a simple immunolabelling method that juxtaposes the expression pattern of claustrum-enriched and cortical-enriched markers. We determined that claustrum cells immunoreactive for the claustrum-enriched markers Nurr1 and Nr2f2 are devoid of the cortical marker Tle4, which allowed us to differentiate the claustrum from adjoining cortical cells. Using retrograde tracing, we verified that nearly all claustrum projection neurons lack Tle4 but expressed Nurr1/Nr2f2 markers to different degrees. At neonatal stages between 7 and 21 days, claustrum projection neurons were identified by their Nurr1-postive/Tle4-negative expression profile, a time-period when other immunolabelling techniques used to localize the claustrum in adult mice are ineffective. Finally, exposure to environmental novelty enhanced the expression of the neuronal activation marker c-Fos in the claustrum region. Notably, c-Fos labeling was mainly restricted to Nurr1-positive cells and nearly absent from Tle4-positive cells, thus corroborating previous work reporting novelty-induced claustrum activation. Taken together, this method will aid in studying the claustrum during postnatal development and may improve histological and functional studies where other approaches are not amenable.
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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) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".