An Intersectional Viral-Genetic Method for Fluorescent Tracing of Axon Collaterals Reveals Details of Noradrenergic Locus Coeruleus Structure
Bibliographic record
Abstract
Understanding the function of broadly projecting neurons depends on comprehensive knowledge of the distribution and targets of their axon collaterals. While retrograde tracers and, more recently, retrograde viral vectors have been used to identify efferent projections, they have limited ability to reveal the full pattern of axon collaterals from complex, heterogeneous neuronal populations. Here we describe TrAC (tracing axon collaterals), an intersectional recombinase-based viral-genetic strategy that allows simultaneous visualization of axons from a genetically defined neuronal population and a projection-based subpopulation. To test this new method, we have applied TrAC to analysis of locus coeruleus norepinephrine (LC-NE)-containing neurons projecting to medial prefrontal cortex (mPFC) and primary motor cortex (M1) in laboratory mice. TrAC allowed us to label each projection-based LC-NE subpopulation, together with all remaining LC-NE neurons, in isolation from other noradrenergic populations. This analysis revealed mPFC-projecting and M1-projecting LC-NE subpopulations differ from each other and from the LC as a whole in their patterns of axon collateralization. Thus, TrAC complements and extends existing axon tracing methods by permitting analyses that have not previously been possible with complex genetically defined neuronal populations.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".